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Record W1524460852 · doi:10.1111/iere.12278

TESTING THE THEORY OF MULTITASKING: EVIDENCE FROM A NATURAL FIELD EXPERIMENT IN CHINESE FACTORIES

2018· article· en· W1524460852 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

VenueInternational Economic Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaShanghai University of Finance and Economics
KeywordsSalaryHuman multitaskingNatural experimentIncentiveQuality (philosophy)EconomicsField (mathematics)Margin (machine learning)Factory (object-oriented programming)EconometricsPiece workOperations managementLabour economicsMicroeconomicsComputer scienceStatisticsPsychologyMathematicsCognitive psychology

Abstract

fetched live from OpenAlex

Abstract Using a natural field experiment, we quantify the impact of one‐dimensional performance‐based incentives on incentivized (quantity) and nonincentivized (quality) dimensions of output for factory workers with a flat‐rate or a piece‐rate base salary. In particular, we observe output quality by hiring quality inspectors unbeknownst to the workers. We find that workers trade off quality for quantity, but the effect is statistically significant only for workers under a flat‐rate base salary. This variation in treatment effects is consistent with a simple theoretical model that predicts that when agents are already incented at the margin, the quantity–quality trade‐off resulting from performance pay is less prominent.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.107
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.414
Teacher spread0.315 · 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