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Record W1732231600 · doi:10.1002/meet.2014.14505101108

Interdisciplinarity patterns of highly‐cited papers: A cross‐disciplinary analysis

2014· article· en· W1732231600 on OpenAlex
Shiji Chen, Yves Gingras, Clément Arsenault, Vincent Larivière

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsDisciplineEngineering ethicsSpecialtyNatural scienceSociologySocial scienceCross disciplinaryEpistemologyPsychologyEngineeringData scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

ABSTRACT This study analyzes the level of interdisciplinarity and interspecialty of highly cited papers. We distinguish research referring to different disciplines (referred to as “interdisciplinarity”) and research referring to different specialties of the same discipline (referred to as “interspecialty”). The results indicate that: (1) interspecialty research, has a greater impact on science development than intradisciplinary (or intraspecialty) research for most specialties and disciplines; (2) interdisciplinary research plays a more important role in Natural Sciences and Engineering than in Social Sciences and Humanities; and (3) interdisciplinary research is becoming more important in science either at the specialty or discipline level.

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.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.014
Science and technology studies0.0010.004
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
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 head0.025
GPT teacher head0.372
Teacher spread0.347 · 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