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Record W4244543711 · doi:10.1007/s40881-018-0059-0

Instructions

2018· article· en· W4244543711 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

VenueJournal of the Economic Science Association · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser University
KeywordsReinforcementComputer scienceReinforcement learningTask (project management)Stochastic gameBaseline (sea)ComprehensionArtificial intelligencePsychologySocial psychologyMathematics

Abstract

fetched live from OpenAlex

Abstract A survey of instruction delivery and reinforcement methods in recent laboratory experiments reveals a wide and inconsistently reported variety of practices and limited research evaluating their effectiveness. Thus, we experimentally compare how methods of delivering and reinforcing experiment instructions impact subjects’ comprehension and retention of payoff-relevant information. We report a one-shot individual decision task in which non-money-maximizing behavior can be unambiguously identified and find that such behavior is prevalent in our baseline treatment which uses plain, but relatively standard experimental instructions. We find combinations of reinforcement methods that can eliminate half of non-money-maximizing behavior, and we find that we can induce a similar reduction via enhancements to the content of instructions. Residual non-money-maximizing behavior suggests that this may be an important source of noise in experimental studies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.998

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.343
Teacher spread0.320 · 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