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Record W4248420672 · doi:10.1007/s10683-012-9318-8

Multitasking

2012· article· en· W4248420672 on OpenAlexfundno aff
Thomas Buser, Noemi Peter

Bibliographic record

VenueExperimental Economics · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersMagyar Tudományos AkadémiaUniversiteit van AmsterdamYork University
KeywordsHuman multitaskingScheduleComputer scienceScheduling (production processes)PsychologyCognitive psychologySocial psychologyOperations managementEngineeringOperating system

Abstract

fetched live from OpenAlex

Abstract We examine how multitasking affects performance. We also examine whether individuals optimally choose their degree of multitasking or whether they perform better under an externally imposed schedule. Subjects in our experiment perform two different tasks according to one of three treatments: one where they perform the tasks sequentially, one where they are forced to multitask, and one where they can freely organize their work. Subjects who are forced to multitask perform significantly worse than those forced to work sequentially. Surprisingly, subjects who can freely organize their own schedule also perform significantly worse. These results suggest that scheduling is a significant determinant of productivity. Finally, our results do not support the stereotype that women are better at multitasking. Women suffer as much as men when forced to multitask and are actually less inclined to multitask when being free to choose.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.358
Teacher spread0.303 · 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 designObservational
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

Citations95
Published2012
Admission routes1
Has abstractyes

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