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Record W3025772844 · doi:10.1257/pandp.20201093

Stories at Work

2020· article· en· W3025772844 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.

Bibliographic record

VenueAEA Papers and Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsIncentivePairingVariety (cybernetics)Quality (philosophy)Work (physics)Simple (philosophy)Public relationsBusinessComputer scienceMicroeconomicsEconomicsPolitical scienceEngineeringEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

Organizational stories are commonplace and a crucial force shaping employee behavior. We show how an organization's choice of story can be formally incorporated into its design problem. In our simple model, the organization optimally becomes either “purpose driven,” which involves pairing flat money incentives with a story that emphasizes the importance of generating output (e.g., saving lives, putting a person on the moon), or “incentive driven,” which involves pairing steep money incentives with a narrower story that emphasizes the importance of maintaining ethical standards (e.g., maintaining quality, helping peers). We illustrate the applicability of these results using a variety of examples.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.302

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 head0.049
GPT teacher head0.302
Teacher spread0.254 · 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