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Record W1849438777 · doi:10.3386/w19694

Does Knowledge Accumulation Increase the Returns to Collaboration?

2013· report· en· W1849438777 on OpenAlex
Ajay Agrawal, Avi Goldfarb, Florenta Teodoridis

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

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaAdvance HEUniversity of TorontoCarnegie Mellon University
KeywordsBusinessKnowledge managementComputer science

Abstract

fetched live from OpenAlex

We conduct the first empirical test of the knowledge burden hypothesis, one of several theories advanced to explain increasing team sizes in science. For identification, we exploit the collapse of the USSR as an exogenous shock to the knowledge frontier causing a sudden release of previously hidden research. We report evidence that team size increased disproportionately in Soviet-rich relative to -poor subfields of theoretical mathematics after 1990. Furthermore, consistent with the hypothesized mechanism, scholars in Soviet-rich subfields disproportionately increased citations to Soviet prior art and became increasingly specialized.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.005

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 head0.272
GPT teacher head0.485
Teacher spread0.213 · 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