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Record W1500035715 · doi:10.3386/w20037

The (Changing) Knowledge Production Function: Evidence from the MIT Department of Biology for 1970-2000

2014· report· en· W1500035715 on OpenAlexaff
Annamaria Conti, Christopher Liu

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProduction (economics)Function (biology)Knowledge productionBiologyComputer scienceKnowledge managementEvolutionary biologyEconomics

Abstract

fetched live from OpenAlex

Considerable attention has been focused, in recent years, on the role that graduate and postdoc students play in the production of academic knowledge. Using data from the MIT Department of Biology for the period 1970-2000, we analyze the evolution over time of four fundamental aspects of their productivity: i) training duration; ii) time to a first publication; iii) productivity over the training period; and iv) collaboration with other scientists. We identified four main trends that are common to graduate students and postdocs. First, training periods have increased for later cohorts of graduate and postdoc students. Second, later cohorts tend to publish their initial first-author article later than the earlier cohorts. Third, they produce fewer first-author publications. Finally, collaborations with other scientists, as measured by the number of coauthors on a paper, have increased. This increase is driven by collaborations with scientists external to a trainee's laboratory. We interpret these results in light of the following two paradigms: the increased burden of knowledge that later generations of scientists face and the limited availability of permanent academic positions.

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)0.018
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.397
GPT teacher head0.452
Teacher spread0.055 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2014
Admission routes1
Has abstractyes

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