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Record W4396899401 · doi:10.1007/s10683-024-09824-2

Task completion without commitment

2024· article· en· W4396899401 on OpenAlexafffund
David Freeman, Kevin Laughren

Bibliographic record

VenueExperimental Economics · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsQueen's UniversitySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTask (project management)Organizational commitmentPsychologyCompletion (oil and gas wells)Social psychologyEconomicsManagement

Abstract

fetched live from OpenAlex

We conduct an experiment where participants make choices between completing a task now or waiting to complete it in the future. We vary the dates when a task can be completed and the effort required at each date. We infer participants' preferences for when to complete a task and their expectations about how their future preferences will differ from their current ones. Our findings indicate that most participants prefer to complete tasks immediately, even if it demands more effort than waiting. Their choices generally align with the principles of time consistency, monotonicity, and time invariance. We show that quasi-hyperbolic discounting, anticipatory utility, fixed costs, decision costs, and cost-of-keeping-track are all unable to provide a reasonable account of both our findings and related experiments. Supplementary Information: The online version contains supplementary material available at 10.1007/s10683-024-09824-2.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.003

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 head0.156
GPT teacher head0.431
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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