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Record W2951610038 · doi:10.1002/asi.21226

On the relationship between interdisciplinarity and scientific impact

2009· article· en· W2951610038 on OpenAlex

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

VenueJournal of the American Society for Information Science and Technology · 2009
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCitationMainstreamDisciplineCitation analysisScientific literatureCitation impactSocial scienceWeb of scienceImpact factorInterdisciplinaritySociologyEpistemologyEngineering ethicsData scienceComputer sciencePolitical scienceLibrary scienceMEDLINEEngineeringBiologyLawPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article analyzes the effect of interdisciplinarity on the scientific impact of individual articles. Using all the articles published in Web of Science in 2000, we define the degree of interdisciplinarity of a given article as the percentage of its cited references made to journals of other disciplines. We show that although for all disciplines combined there is no clear correlation between the level of interdisciplinarity of articles and their citation rates, there are nonetheless some disciplines in which a higher level of interdisciplinarity is related to a higher citation rates. For other disciplines, citations decline as interdisciplinarity grows. One characteristic is visible in all disciplines: Highly disciplinary and highly interdisciplinary articles have a low scientific impact. This suggests that there might be an optimum of interdisciplinarity beyond which the research is too dispersed to find its niche and under which it is too mainstream to have high impact. Finally, the relationship between interdisciplinarity and scientific impact is highly determined by the citation characteristics of the disciplines involved: Articles citing citation‐intensive disciplines are more likely to be cited by those disciplines and, hence, obtain higher citation scores than would articles citing non‐citation‐intensive disciplines.

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)0.028
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.093
Science and technology studies0.0020.004
Scholarly communication0.0020.002
Open science0.0020.000
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 head0.348
GPT teacher head0.553
Teacher spread0.205 · 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