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Record W2621068011 · doi:10.29173/cais756

The Problem of Tradition: Teaching Research in the Shadow of the History of Science

2013· article· fr· W2621068011 on OpenAlexvenueno aff
John M. Budd

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2013
Typearticle
Languagefr
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesEnlightenmentMaterialismPhilosophySociologyEpistemology

Abstract

fetched live from OpenAlex

There is a research tradition that builds heavily upon traditions that began in the early days of the Enlightenment. One manifestation of the tradition is in the search for, and treatment of, evidence. The paper will present a content analysis of the syllabi of research-related courses in ALA-accredited master’s programs, which demonstrates the reliance on the Enlightenment materialist tradition. Preliminary examination suggests reliance on, among other things, behavioristic observations of information seekers and users, cognitive investigation that is limited to eliminativist or reductionist methods, or constructivist approaches that likewise reduce analytical possibilities.Il existe une tradition de recherche dont les fondements remontent au début du siècle des Lumières. Une manifestation de cette tradition se retrouve dans la recherche et dans le traitement de la preuve. Cet article présente une analyse de contenu des plans de cours des cours de recherche enseignés dans le cadre des programmes de maîtrise agréés par l'ALA, démontrant le recours à la tradition matérialiste des Lumières. Un examen préliminaire laisse entendre l'usage notamment d'observations béhavioristes des chercheurs et des utilisateurs d'information, d'investigations cognitives limitées aux méthodes éliminativistes ou réductionnistes ou d'approches constructivistes qui réduisent d'autant les possibilités d'analyse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.077
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.009
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.199
GPT teacher head0.379
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicStatistics Education and MethodologiesFrench-language works237,207