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Record W3038988774 · doi:10.3982/te2963

Production priorities in dynamic relationships

2020· article· en· W3038988774 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

VenueTheoretical Economics · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsWorkplace Health, Safety and Compensation CommissionQueen's UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommitPrincipal (computer security)Production (economics)Scope (computer science)Compensation (psychology)MicroeconomicsIncentiveEconomicsRisk analysis (engineering)BusinessComputer scienceComputer security

Abstract

fetched live from OpenAlex

We characterize optimal contracts in a dynamic principal–agent model of joint production in which project opportunities are heterogenous, utility from these projects is nontransferable, and the agent has the option to quit the relationship at any time. To demand the production of projects that benefit her but not the agent, the principal must commit to produce projects that benefit the agent in the future. Production at all stages of the relationship is ordered by projects' cost‐effectiveness , which is their efficiency in transferring utility between the principal and the agent: cost‐effective demands impose relatively low costs on the agent and cost‐effective compensation imposes relatively low costs on the principal. Over time, optimal contracts become more generous toward the agent by adding commitments to less cost‐effective compensation. In turn, because this new compensation cannot be profitably exchanged against less cost‐effective demands, the principal narrows the scope of her demands.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.827
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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