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Record W4387770435 · doi:10.1093/scipol/scad064

Gerontocracy, labor market bottlenecks, and generational crises in modern science

2023· article· en· W4387770435 on OpenAlex
Kyle Siler

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

VenueScience and Public Policy · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsPosition (finance)RentingLabour economicsWork (physics)Face (sociological concept)OddsTrack (disk drive)BusinessEconomicsPolitical scienceSociologyEngineeringFinance

Abstract

fetched live from OpenAlex

Abstract Many early career researchers (ECRs) currently face long odds of attaining a full-time or tenure-track research position. Populations of graduate and postdoctoral researchers have continually increased, without concomitant increases in tenure-track jobs or stable research careers. The current hypercompetitive academic labor market is societally inefficient and often inhumane to ECRs, commonly characterized by precarious, exploitative, and/or uncertain employment terms. Compounding generational disadvantages endured by many ECRs at work, analysis of worldwide data on housing rental costs reveals that escalating costs of living are an especially acute problem for ECRs, since major research universities tend to be located in expensive cities. The unfavorable plight of today’s ECRs can be partly attributed to the disproportionate zero-sum distribution of resources to senior academics, particularly of the baby boomer generation. The uncertainty, precariousness, and hypercompetitiveness of ECR academic labor markets undermine the quantity and quality of scientific innovations, both in the present and in the future.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearch
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.062
GPT teacher head0.297
Teacher spread0.235 · 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