Epistemological Problems of Economics (2nd Edition)

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Speaking of things that might require Combat Epistemology training. You just feel those Gs and know that if anything goes wrong its instant carnage. Who in their right mind would dare to be riding the ancient rails of the NY Central Railway on a mph bullet train? But Given the Marquette National Bank v. Or did you think the griper thought the whole thing up himself. But yes he was the worst president we have ever known — including Presidents Buchanan and Bush.

The Acela is limited in speed due the crossings and proximity to residential areas in CT, that have kepts its speed significantly less than how it was sold. It is not due to its infrastructure. There is nothing preventing the train from reaching the speeds it is designed to reach, if safety for humans outside of the train were not considered. Ravi Bhatra, whom I believe you know, said on the Thom Hartmann radio show last week that Goldman, Citi, and the rest have invested heavily in oil speculation.

K, and your commenters are so adept at. Maglev seems to be running OK in Shanghai. Now, to work on my prose….. I was a wee 28 year-old asset manager in Boston in , but I remember well that St.

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Germain was Chairman of the House Banking Committee. There are also some interesting, and legitimate economic speculations underlying the book — especially the first half. I felt the world shift beneath my feet. You also do not fully understand the savings and loan crisis, or rather you have cherry-picked certain aspects that support your highly partisan views. Spending ballooned, more money was allocated to social security and the department of education, the size of government grew overall, and he actually did very little to promote free trade.

First , this kind of social constructionism views linguistic forms, discourses and discursive practices as all pervasive and as constitutive of social relations, practices and processes. Constructionists of this kind fail to acknowledge, or explicitly declare, that there cannot be any separation between discursive and non- or extra discursive dimensions of social reality see Laclau and Mouffe To be more precise, they fail to acknowledge that in many cases discourse can have access to realities beyond it.

Moreover, they cannot imagine or account for that discourses may be simultaneously constitutive of social phenomena and constituted by realities ontologically existent separately from them. Thus, this version of social constructionism is a kind of discursive reductionism, which do not allow for the account and researching of the whole range of social complexity and ontological depth of social reality.

Second, radical social constructionism is based on a view of a much contested and critiqued view of language as a closed, self-referential system of signs see Archer ; Potter , ignoring the referential potentialities of language and discourse and the role of practice in social life, which always exceeds and goes beyond linguistic forms and constructions Sayer ; Nellhaus ; Fairclough et al.

Finally , radical social constructionism cannot avoid internal inconsistencies and contradictions and, especially, relativism, which undermines critical and emancipatory social research. This is because relativist thinking connects any assertion about social reality with a certain conceptual scheme, which is rendered equally arbitrary and conventional with all others and thus does not allow for any kind of access to mind-independent realities see Hammersley But as Hammersley , p.


Moreover, the inability to adjudicate between different discourses through relating them to extra-discursive realities is not resolved and continues to be a major problem even when the criteria of judging between different situations and conditions are ethical and moral. This is because relativist and discourse-reductionist thinking is all embracing and is not possible to limit it to the cognitive-epistemic realm. Thus, Hammersley , p. Now, the limitations and fallacies of this kind of thinking and its disadvantages as regards social-scientific, critical, emancipatory qualitative research practice on migration can be indicated through the example of securitization research.

Securitization means the conceptualisation and treatment of immigrants as a threat and problem, and is connected with illegalization and criminalization of migration and the whole range of associated actions and policies against cross-border mobility by certain categories of people see Karyotis and Skleparis Squire , following partially this line of thinking and adopting the discourse theory of Laclau and Mouffe declares that: …there exists a world and objects that are independent of discourse, but that these are only accessible through a discursive frame.

And elsewhere: There are thus two ontological assumptions on which an anti-objectivist theory of securitization is based. First , it equates the positivist notion of objectivity with objectivity in general. It cannot imagine that there may be certain inter-subjective discourses and certain conceptual schemes and frameworks that grasp extra-discursive realities quite accurately. Actually, in many instances, the points of view of the dominated and exploited, such as those of contemporary criminalized or illegalized migrants and the theoretical elaborations based on them, can grasp broader realities of capitalist relations across different geographical scales extremely accurately, because they derive from specific positions in social hierarchies see Danermark et al.

Second , it leans towards epistemic and inevitably moral relativism. For if discourses construct reality and we have access only to them and not to the extra-discursive realm, then what are exactly the criteria of choosing among securitizing and non-securitizing discourses? As we indicated above, within relativist frameworks of thinking, there are not epistemic, cognitive or moral criteria to do so.

Critical realism offers a radically different notion of objectivity and stresses that concept mediation of knowledge and reality does not preclude truth and objectivity. As Moya , p. Now moving at the level of social actors and relations, critical realist ontological and epistemological approach views subjectivity and inter-subjectivity as integral parts of reality. So, subjectivity and inter-subjectivity can be approached in a way that valid and objective although fallible knowledge about them is possible.

Moreover, subjective and inter-subjective beliefs, meanings and interpretations of reality and social experiences may vary in how they accurately describe realities beyond them. Qualitative research has an invaluable role and can contribute significantly to the assessment of accuracy of such meanings and interpretations as both more or less accurate interpretations play a vital role in social change or stasis. For realist qualitative researchers then, subjective and inter-subjective meanings and interpretations along with perceptions connected to personal and social identity are treated as theories about reality, which can be assessed for their truthfulness and validity Iosifides a.

Thus, …there is a cognitive component to identity that allows for the possibility of error and of accuracy [emphasis added] in interpreting the things that happen to us. Some beliefs, subjective meanings and public discourses have to be necessarily and objectively false in order to contribute to the reproduction of objectively real and true conditions of domination and exploitation. For example, racism of any form and type and form, either every day, institutional, collective or political, is one such set of interpretations and discourses which tend to mystify reality and obscure real relations of domination and exploitation.

Combined with the needs of capitalist accumulation in different spatial scales and with political efforts for popular mobilization through scapegoating and systematically devaluating certain categories of people, racist ideas, beliefs, interpretations and discourses are contributing significantly to the production and reproduction of power inequalities and injustice. As such, realist qualitative researchers pay acute attention to them and conceptualize them as utterly wrong and false and not as just different constructions of reality derived from self-referent signification systems see Carter Moreover, researchers of this kind take into serious account the complexity of subjectivity formation and re-formation in time and causally connect subjectivity, inter-subjectivity, experiences and social positions of social actors see Moya ; Walby In migration research, matters of relations between the researcher s and research participants, reflexivity and positionality are of great importance, as they are part of or influence significantly the theoretical and conceptual frameworks at hand, the ways that data are collected and produced and the approaches within which findings are interpreted and presented see Denzin and Lincoln ; Iosifides Qualitative researchers who investigate migration are or have to be extremely sensitive about such issues, as they are usually more intensely involved with the lives of people who, in most cases, found themselves in more disadvantaged positions in social hierarchies.

Thus, matters of power differences between researcher s and research participants and a constant preoccupation with the positionality of the researcher s that is, with the role of points of view, conceptual frameworks and theoretical categories have to be at the center of qualitative inquiry. As migration researchers, being reflexive about our own positions in social settings, our own thought categories, beliefs, emotions, points of view and conceptual schemes, has to be an explicit and vital part of our research endeavors.

Nevertheless, reflexivity and positionality can be exercised within different ontological and epistemological frameworks and can lead to different outcomes. Within certain versions of interpretivist and constructionist thinking analyzed in the previous section, reflexivity and positionality lead to the adoption of the idea according to which it is impossible to move beyond the specificities of various positionalities and points of view in order to discover how things work in reality see Sayer On the contrary, for the critical realist meta-theoretical approach, reflexivity and positionality are not in contradiction with the goal of discovering the workings of real causal powers, but a basic part of it.

Critical realists stress that some positions and some conceptual frameworks are more privileged than others in guiding researchers to explain social phenomena and processes more adequately see Danermark et al. Thus, the task of qualitative researchers influenced by critical realism is, through intense reflexive work, to find and adopt the adequate position and conceptual framework in order to explain phenomena and processes.

I think that in the case of migration studies, this reflexivity work has to be done for the research field as a whole. I mean that, as qualitative migration researchers, we have to be reflexive about the conceptual and theoretical categories we use in order to study migratory phenomena, with the goal to adopt those frameworks which illuminate migration realities more truthfully and adequately. This presupposes abandoning the so called methodological nationalist way of thinking about and theorizing migration and avoiding thinking and theorizing cross-border mobility through the lens of state and other dominant categories, discourses and interpretations Amelina et al.

It also presupposes the adoption of the perspective of the subaltern, the dominated and the exploited and its refinement to theoretical schemes, which can explain contemporary migratory phenomena and simultaneously critique underlying real mechanisms of exploitation, domination, exclusion and injustice. Methodological nationalism guides research practice, either quantitative or qualitative, which takes dominant categories and conceptualizations as given and natural and researchers influenced by it investigate social reality, including migratory phenomena and processes, as if state and national conceptualizations were true.

Its falsity lies in the fact that it obscures the workings of real causal powers whose interaction produce certain categories of people who are described, labeled and treated as migrants. Amelina, A. Beyond methodological nationalism. Research methodologies for cross-border studies. New York: Routledge. Google Scholar. Andriakaina, E. Beyond positivism and postmodernism. Essays in historical sociology. Patras: Opportuna in Greek. Archer, M. Being human. The problem of agency. Cambridge: Cambridge University Press. Bhaskar, R. Dialectic: The pulse of freedom. London: Verso.

Philosophy and scientific realism. Archer, R. Bhaskar, A. Collier, T. Norrie Eds. London: Routledge. Carter, B. Realism and racism. Concepts of race in sociological research. Cruickshank, J. Realism and sociology. Anti-foundationalism, ontology and social research. Abingdon: Routledge.

Danermark, B. Explaining society. Critical realism in the social sciences. Denzin, N. Handbook of qualitative research. Newbury Park: Sage. Doty, R. Anti-immigrantism in western democracies. Statecraft, desire and the politics of exclusion. Elder-Vass, D. The causal power of social structures: Emergence, structure and agency.

The reality of social construction. Fairclough, N. Analysing discourse. Textual analysis for social research. Discourse analysis in organizational studies: The case for critical realism. Organization Studies, 26 6 , — Critical realism and semiosis. Roberts Eds. Abington: Routledge. Fleetwood, S. Ontology in organization and management studies: A critical realist perspective.

Organization, 12 2 , — Geiger, M. The new politics of international mobility. Migration management and its discontents. Genova, D. Annual Review of Anthropology, 31 , — Georgi, F. Migration management and its discontents pp. Glick Schiller, N. The Ecological Metadata Language EML is a standardized metadata description language for the generation of metadata in the domain of environmental sciences and was the standard which the LTER research community decided to adopt when it engaged in the process of standardizing its scientific data management practices.

Millerand and Bowker described two narratives on how the implementation of the standard was received or perceived :.

However, everybody seems enthusiastic about its implementation. Millerand and Bowker , :. So, on one level everybody was enthusiastic because they could see great possibilities for new kinds of research questions and answers. But when it came to the implementation at their own site, they discovered losses if their own local standards had to be changed to the new standard.

The conclusion that Millerand and Bowker draws from their case study is that the standard had to be redefined and be the object of multiple versions over the course of its development. The implementation of EML is not simply a case of upgrading an existing system.

It consists above all in redefining the sociotechnical infrastructure which supports this articulation of technical, social and scientific practices and : Both standards and ontologies the one apparently technical and the realm of machines, the other apparently philosophical and the realm of ideas need to be socially, organizationally bundled — not as a perpetual afterthought but as an integral necessity.

A simpler conclusion can be made: metadata organized for one purpose may not be optimal for other purposes. Any use of data should ideally have metadata and other procedures and tools optimized for the specific purpose. When new standards serving other and broader goals are implemented, discordant voices arise when locally developed metadata have to fit local goals less in order to serve other interests. A core sentence in the article Millerand and Bowker , is this:. Laporte However, from the point of view of social epistemology, this will forever remain just a dream.

What is possible, is to construe standards which are carefully developed to support specific goals — or compromises based on careful negotiations between different goals, which have first to be identified. This imply a movement of focus from a narrow perspective to a broader social and philosophical perspective. Database semantics is about the meaning of data in databases. The field of data semantics seems to be well established in computer science. In the Journal of Data Semantics was established, and in its first editorial Stuckenschmidt , 1 wrote:.

What is important, however cf. Section 3. In standard databases this is solved by having titles and printing in separate fields, to which searches can be specified. This is a simple case of semantic holism, the principle that the meaning of a word or sign depends on the context in which it is located. Example: Paradigm one: Ptolemaic astronomers might learn the concepts "star" and "planet" by having the Sun, the Moon, and Mars pointed out as instances of the concept "planet" and some fixed stars as instances of the concept "star". Paradigm two: Copernicans might learn the words star , planet , and satellites by having Mars and Jupiter pointed out as instances of the concept "planet", the Moon as an instance of the concept "satellite", and the Sun and some fixed stars as instances of the concept "star".

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Thus, the terms star , planet , and satellite got a new meaning and astronomy got a new classification of celestial bodies. This means that if different paradigms are represented in the same database, that database may use the same terms in different ways. Of course, as Stuckenschmidt wrote , 3 , the systems designer in closed systems may standardize the meanings by using controlled vocabularies, CV , which however, introduces a new layer of interpretation that users have to relate to.

This is an issue that has been neglected in much literature on CV. In order to understand database semantics, we need therefore to consider how the meanings of terms are applied in the primary literature and from there consider how these meanings are represented in the secondary literature such as bibliographical databases. The Web is probably a much better approximation to how meanings are developed in different kinds of discourses. How can we work with database semantics in open systems, such as the WWW?

Stuckenschmidt mentions the use of a jointly shared ontology, a top-level ontology or the use of semantic mappings [40]. Semantics from data This is, for example, about the use of statistical approaches, linguistic approaches and learning approaches to extract semantic information from data sets. Semantics from users This is described by Stuckenschmidt 4 as a new trend in data semantics. The idea is more that asking many users the same question will ultimately generate the right result because a majority of users will give the correct interpretation of the piece of data.

These approaches outlined by Stuckenschmidt seem to be based on the rationalist idea of one neutral and best interpretation of concepts: a view from nowhere rather than a view from a particular perspective. There seem to be a lack of understanding that semantic relations are not neutral but related to specific goals, interests, and paradigms etc. That means, that there is a danger that using a given approach — e.

From the point of view of social epistemology, the core issue with databases is that data are taken out of some contexts and put into other contexts, where the different contexts may represent implicit knowledge or conflicting perspectives, paradigms, interests and goals; as put by Leonelli : data make journeys, they travel. Such journals have to be considered from the perspective of semantic holism. The implication of this view is that an important issue in data semantics is to uncover the main different meaning structures paradigms, perspectives or voices in databases, on the web and in different fields of knowledge.

One should not start by studying the data isolated but start by studying their overall contexts in a top-down fashion or interactively top-down and bottom-up. However, if the records in PsycInfo are merged with the records in Chemical Abstracts , you will have to change your search strategy and specify that you are searching studies on how lead influences behaviour and performance. This new strategy would probably be less than optimal regarding the part of the records originally indexed in PsycInfo because implicit information is lost by the merging.

At another level PsycInfo can be seen as a merging of records which were once presented in individual journals, some of which may be American, some European, some behaviouristic, other psychoanalytic, etc. Originally, to the readers of those journals their selection policy and their way of writing titles and composing articles reflected some implicit meanings in those journals.

Data, big data and database semantics (IEKO)

By making a controlled vocabulary, a classification scheme, a certain structure in the records and so on the people behind PsycInfo made certain decisions which were coloured by their view of knowledge. In other words, a given set of texts represents different paradigms [41] or voices as understood by Bakhtin ; [42] , which should be identified and thereby knowledge organization and information retrieval should provide choices for users to consciously select given voices based on informed choices.

In a way this corresponds to the former mentioned terms variety , diversity , heterogeneity and messiness , but from the perspective that this diversity contains different values and may have their own conceptual structures, which should be identified and made visible. The more heterogenous the big data sets are, the more implicit knowledge is lost, and the more important the reconceptualization becomes.

Leonelli is a paper from the philosophy of biology which is based on an idea related to the idea of merging and re-identify conceptual structures. This sort of information, typically referred to as metadata Edwards et al. Data re-use. After having been de-contextualised and recontextualised, data are therefore supposed to fulfil their epistemic role by leading to a variety of new discoveries. This is an extremely hard task, given that curators want to leave the interpretation of the potential evidential value of data as open as possible to database users.

Ideally, curators should label data according to the interests and terminology used by their prospective users, so that a biologist is able to search for any data connected to her phenomenon of interest e. The important conclusions are of this section are: Databases are supposed to answer a broad range of different needs, but nonetheless, the way metadata are assigned or not assigned , and data thereby classified, determines partly the ways the database can fruitfully be used.

Data are always produced for some purposes and perspectives. To make database effective data must be classified and coded. But this classification cannot be neutral in relation to the purposes for which the database is going to be used. This means that the coding of data reestablishes some of the implicit knowledge lost during the merging. The examination of the issues related to data and big data confirms the need to look at different domains. The relevance of epistemological perspectives exists at two levels:. We find that the best theoretical frame to study big data is an approach which recognizes the importance of epistemology, which is a much-neglected perspective today.

We should always consider how we in LIS conceptualize the phenomena, we study, including data and big data cf. Johansson , 28ff. This is an argument for more conceptual, theoretical and philosophical studies cf. Furner In modern non-scientific use, however, it is generally not treated as a plural. Instead, it is treated as a mass noun, similar to a word like information , which takes a singular verb.

Other professional organizations and style guides require that authors treat data as a plural noun. For example, the Air Force Flight Test Center specifically states that the word data is always plural, never singular. Some major newspapers such as the New York Times use it either in the singular or plural. In the New York Times the phrases "the survey data are still being analyzed" and "the first year for which data is available" have appeared within one day.

Theory and History (Chapter 14: The Epistemological Features of History) by Ludwig von Mises

In scientific writing data is often treated as a plural, as in These data do not support the conclusions, but it is also used as a singular mass entity like information. British usage now widely accepts treating data as singular in standard English, including everyday newspaper usage at least in non-scientific use. UK scientific publishing still prefers treating it as a plural. Some UK university style guides recommend using data for both singular and plural use and some recommend treating it only as a singular in connection with computers".

Graphic record was, for example, used by Shera , 80 and elsewhere. Rider estimated that American university libraries were doubling in size every sixteen years. The rapid pace of scientific and technical advances that were accumulating since the start of the 20th century, produced by midcentury a scientific and technical revolution. The term data science sounds, from a Danish ear in particular, somewhat strange, because computer science in Danish is datalogi meaning the study of data. Defining data Where definitions of data are offered these are typically clearly and succinctly stated, sometimes with examples.

In summary the definitions variously suggest that: Data has no meaning or value because it is without context and interpretation [27, 40—42]. Data are discrete, objective facts or observations, which are unorganized and unprocessed, and do not convey any specific meaning [20; 37; 38; 41]. Data items are an elementary and recorded description of things, events, activities and transactions [43—45].

Choo [25] suggests that data are often elements of larger physical systems such as books, or instrument panels which give clues about what data to notice and how they should be read. Jashapara [39] and Choo [25] also introduce the concept of signals. Jashapara [39] suggests that we acquire data from the external world through our senses and try to make sense of these signals through our experience. Choo [25] develops this further and specifically identifies signals as the origin of data, and proposes the processes of sensing and selecting, together described as physical structuring, as transforming signals into data.

Interestingly, these definitions are largely in terms of what data lacks; data lacks meaning or value, is unorganized and unprocessed. Concerning the development of the distinction between data and information Gray is relevant although that article only covers findings from the information systems literature. The reliability of DNA test depends on different issues, including how the test was done and how closely related the persons are.

The quote about Mr. It was denied strenuously that information was being retrieved. The situation has changed. We believe that the purpose of an information retrieval system is to provide information about a request and that a request is a representation of an information need that an IR system attempts to satisfy.

Hence, a fundamental problem is how to compute the information contained in one object e. Rijsbergen and Lalmas seems to be the only serious argument that information retrieval can be justified as a correct term, and it seems itself related to the empiricist and positivist philosophy which was rejected by Capurro in Zins , and by many others.

What are raw and unprocessed data to one person may be meaningful information to another. A data element is the smallest unit of information to which reference is made. Data in a database may be characterized as predominantly word oriented e. Word oriented, numeric, image, and sound databases are processed by different types of software text or word processing, data processing, image processing, and sound processing.

Data can also be referred to as raw , processed , or verified. Raw data consist of original observations, such as those collected by satellite and beamed back to Earth, or initial experimental results, such as laboratory test data. After they are collected, raw data can be processed or refined in many different ways. Processing usually makes data more usable, ordered, or simplified, thus increasing their intelligibility. Verified data are data whose quality and accuracy have been assured.

For experimental results, verification signifies that the data have been shown to be reproducible in a test or experiment that repeats the original. For observational data, verification means that the data have been compared with other data whose quality is known or that the instrument with which they were obtained has been properly calibrated and tested. This definition was adapted by some ISO standards.

Particularly in the case studies, usage follows the conventions of the field being discussed. UlrichsWeb does not list data journal among its serial types, but see Candela et al. The literature about data handbooks is very small, almost non-existing. Among the papers about data-handbooks is Gurr , which is a review of some data-handbooks in political science. Your research data will be given a citable persistent identifier e. Among the published studies on data citation are, for example, Zhao, Erjia and Li An information system either captures data by measuring the attributes of a phenomenon, or it is provided from another information system — be it mechanical e.

However, very similar definitions were formerly used in Wikipedia. Jensen , ix only considered the unit-phenomena in science, but the data concept is also used outside science, and also here are data about units of various kinds. Units need not be about measurements. Owens also discusses the data concept in the humanities. There are different ways to express larger quantities of data as indicated in the quote below, the traditional one being: A byte is 8 bits, A kilobyte KB is 2 10 bytes or 1, bytes; A megabyte MB is 2 20 bytes or 1, kilobytes; A gigabyte GB is 3 30 bytes or 1, megabytes; A terabyte TB is 2 40 bytes or 1, gigabytes; A petabyte PB is 2 50 bytes or 1, terabytes; An exabyte EB is 2 60 bytes or 1, petabytes; A zettabyte ZB is 2 70 bytes or 1, exabytes; A yottabyte YB is 2 80 bytes or 1, zettabytes.

They should not be used to indicate powers of 2 for example, one kilobit represents bits and not bits. The IEC [International Electrotechnical Commission] has adopted prefixes for binary powers in the international standard IEC , third edition, Letter symbols to be used in electrical technology — Part 2: Telecommunications and electronics. The names and symbols for the prefixes corresponding to 2 10 , 2 20 , 2 30 , 2 40 , 2 50 , and 2 60 are, respectively: kibi, Ki; mebi, Mi; gibi, Gi; tebi, Ti; pebi, Pi; and exbi, Ei.

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Zikopoulos et al. A serious field works on construing a set of necessary and sufficient terms that are associated with its basic theories. Social pressure to deal with hot terms rather than with well-constructed terms and theories may indeed be a barrier for scientific progress. Pechenick, Danforth and Dodds and Zhang demonstrate how the interpretation of big data often errors because of missing knowledge about the composition of the data sets.

This is due to problems with OCR-recognition: The lowercase long s in certain old books looks a lot like a f. Pechenick, Danforth and Dodds demonstrates how important it is to know, for example to which degree Google Books cover fiction, nonfiction and scientific literature from different periods.

This example demonstrates one of the core assumptions of the present article, that data need interpretation on the basis of knowledge in specific domains. The reason big data has become a common term is that other characteristics than size are very important for managing data from an IT point of view, but that does not indicate they are important for a theory of data from the point of view of LIS and knowledge organization.

However, the task here is to evaluate the term in the theoretical context of knowledge organization. Whether or not it is useful in other contexts is not a relevant discussion here. This can also be understood as a claim that undiscovered public knowledge exists in the two communities.


As such, this example points to an issue, that was relatively neglected by Swanson: That discovering of relevant knowledge is closely related to issues of scientific paradigms. An anonymous reviewer asked me to back-up the claim that standards supports certain interests at the cost of other interest with references to the literature. The best source is Lampland and Star including the chapter by Millerand and Bowker discussed in this Section 5. Most literature on this topic addresses specific domains, such as the ideology of language standardization Milroy or standardization of accounting rules Ramanna etc.

The general claim is supported by the growing literature about the impossibility of neutral knowledge organization systems e.

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The most important arguments come from voices in epistemology and the philosophy of science. The project is built on the claim that it is possible to automatize semantic analyses of materials to create coherent semantic metadata, which can be used by the machine either by help of an AI inference system or as automatic creation of linked data. Whether this is possible beyond controlled vocabularies within in a formalized semantic universe remains to be seen.

Whereas Kuhn only saw one paradigm at a given time, today the idea of multiple, competing paradigms is the norm. Kuhn did not recognize different schools in the social sciences as paradigms, but this is nonetheless the way paradigms is mostly understood today. Bakhtin ; introduced the concept of "voice" according to which an utterance is always produced by a certain voice, a speaking personality with a specific viewpoint.

Specific voices, being invoked and informed as responses in the conversational and collaborative situation at hand, are also informed by a broader socio-cultural context with a particular history. Such social languages correspond to what Bakhtin called speech genres.

These refer to types of utterances produced by types of voices. Speech genres can be recognized by typical situations of speech communication, by typical themes and meanings of words that are addressed. Advancing a certain theoretical viewpoint involves talking in terms of that theory, although it simultaneously depends on how the specific person understands that theory, what he or she places within that category.

Multivoicedness does not, however, just mean the juxtaposition of voices; not just that persons said what they meant. New meaning, new insight and understanding is, according to Bakhtin, dependent on the tension between different voices, viewpoints and perspectives. Any sign has an orientation toward plurivocality, polylogism, and multivoicedness, and therefore an ability to adapt to new and different situational contexts. Austin, Peter C. Journal of Clinical Epidemiology 61, no. Mamdani, David N. Juurlink, and Janet E. Journal of Clinical Epidemiology 59, no. Bakhtin, Mikhail Mikhailovich.

Austin: University of Texas Press. Beaton, Brian. Scientific American , no. Borgman, Christine L. Boyd, Danah and Kate Crawford. Briet, Suzanne. What is Documentation? English translation of the classic French text. Lanham, MD: Scarecrow Press. Buckland, Michael. Knowledge Organization 45, no. Bugaje, Maryam and Gobinda Chowdhury. Lecture Notes in Computer Science, vol. Cham: Springer, Journal of the Association for Information Science and Technology 66, no. DOI: Caputo, John D. Hermeneutics: Fact and Interpretation in the Age of Information.

London: Penguin. Clarivate Analytics. Cohen, Aaron, Michael, P. International Journal of Medical Informatics 73, no. Consultative Committee for Space Data Systems.


Diebold, Francis. Drucker, Johanna. Digital Humanities Quarterly 5, no. Dye, Lee. Edwards, Paul N. Mayernik and Archer L. Batcheller, Geoffrey C. Bowker and Christine L. Social Studies of Science 41, no. In Press. Theories and Methods in Data Science Librarianship. London: Facet Publishing. To be published March Finnemann, Niels Ole. Knowledge Organization. Claudio Gnoli. Journal of Documentation 59, no. Floridi, Luciano. New York: Macmillan Reference —7. Philosophy and Technology 25, no. Fox, Christopher, Anany V. Levitin and Thomas C Redman. Journal of Information Science 35, no.

In press. Furner, Jonathan. Wiesbaden: Springer,