MétaCan
Menu
Back to cohort
Record W3021626566 · doi:10.3386/w21134

Slack Time and Innovation

2015· preprint· en· W3021626566 on OpenAlexafffund
Ajay Agrawal, Christian Catalini, Avi Goldfarb

Bibliographic record

VenueNational Bureau of Economic Research · 2015
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsCreativityIdeationExtant taxonPromotion (chess)Task (project management)Interpretation (philosophy)Computer scienceMechanism (biology)Knowledge managementBusinessMarketingManagementPsychologyEconomicsPolitical scienceCognitive scienceSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

The extant literature linking slack time to innovation focuses on how slack time facilitates creative activities such as ideation, experimentation, and prototype development.We turn attention to how slack time may enable activities that are less creative but still important for innovation, namely mundane, execution-oriented tasks.First, we document the main effect: a sharp rise in innovative projects posted on a major crowdfunding platform when colleges are on break.Next, we report timing and project type evidence consistent with the causal interpretation that slack time drives innovation.Finally, we present a series of results consistent with the mundane task mechanism but not with the traditional creativity-related explanations.We do not rule out the possibility that creativity benefits from slack time.Instead, we introduce the idea that mundane, execution-oriented tasks, such as those associated with launching a crowdfunding campaign (e.g., administration, planning, promotion), are an important input to innovation that may benefit significantly from slack time.

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)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.001

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 head0.329
GPT teacher head0.455
Teacher spread0.126 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207