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Record W2130456849 · doi:10.1002/asi.23266

Team size matters: Collaboration and scientific impact since 1900

2014· article· en· W2130456849 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2014
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersCanada Research Chairs
KeywordsCitation impactCitationImpact factorInflation (cosmology)Political sciencePsychologyComputer scienceLibrary scienceLaw

Abstract

fetched live from OpenAlex

This article provides the first historical analysis of the relationship between collaboration and scientific impact using three indicators of collaboration (number of authors, number of addresses, and number of countries) derived from articles published between 1900 and 2011. The results demonstrate that an increase in the number of authors leads to an increase in impact, from the beginning of the last century onward, and that this is not due simply to self‐citations. A similar trend is also observed for the number of addresses and number of countries represented in the byline of an article. However, the constant inflation of collaboration since 1900 has resulted in diminishing citation returns: Larger and more diverse (in terms of institutional and country affiliation) teams are necessary to realize higher impact. The article concludes with a discussion of the potential causes of the impact gain in citations of collaborative papers.

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)0.045
metaresearch head score (Gemma)0.158
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.158
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0180.076
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.000
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 head0.095
GPT teacher head0.471
Teacher spread0.376 · 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