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Record W4392508402 · doi:10.1111/joie.12387

Coupling Information Disclosure with a Quality Standard in R&D Contests*

2024· article· en· W4392508402 on OpenAlex
Gaoyang Cai, Qian Jiao, Jingfeng Lu, Jie Zheng

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

VenueJournal of Industrial Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsCONTESTQuality (philosophy)InnovatorEx-anteSet (abstract data type)Information qualityAggregate (composite)BusinessPrivate information retrievalMicroeconomicsEconomicsComputer scienceComputer securityInformation systemFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

We study two‐player R&D contest design using both an information disclosure policy and a quality standard as instruments. The ability of an innovator is known only to himself. The organizer commits ex‐ante to a minimum quality standard and whether to have innovators' abilities publicly revealed before they conduct R&D activities. We find that without quality standards, fully concealing innovators' abilities elicits both higher expected aggregate quality and expected highest quality. With optimally set quality standards, although fully concealing ability information still elicits higher expected aggregate quality, fully disclosing this information leads to a higher level of expected highest quality. Moreover, the optimal quality standards are compared across different objectives and disclosure policies.

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)0.002
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 score0.794
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.106
GPT teacher head0.373
Teacher spread0.267 · 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