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Record W2040419988 · doi:10.1037/a0021086

The graying of academia: Will it reduce scientific productivity?

2010· article· en· W2040419988 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Psychologist · 2010
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityPublicationPolitical sciencePsychologyEconomic growthDemographic economicsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

The belief that science is a young person's game and that only young scientists can be productive and publish high-quality research is still widely shared by university administrators and members of the scientific community. Since the average age of university faculties is increasing not only in the United States but also in Europe, the question arises as to whether this belief is correct. If it were valid, the abolition of compulsory retirement in the United States and some parts of Canada would lower the productivity of these university systems. To address this question, this article reviews research on the association of age and scientific productivity conducted during the last four decades in North America and Europe. Whereas early research typically showed a decline in productivity after the ages of 40 to 45 years, this decline has been absent in more recent studies. Explanations for this change are discussed.

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)0.035
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, 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 score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0120.154
Science and technology studies0.0010.008
Scholarly communication0.0020.001
Open science0.0050.001
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 head0.506
GPT teacher head0.622
Teacher spread0.116 · 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