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Record W4220999739 · doi:10.31234/osf.io/bfr48

Whither Inhibition?

2022· preprint· en· W4220999739 on OpenAlexafffund
Kaitlyn M. Werner, Michael Inzlicht, Brett Q. Ford

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto ScarboroughNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsOperationalizationLeverage (statistics)Construct (python library)Process (computing)Outcome (game theory)PsychologyComputer scienceProcess managementPolitical scienceEpistemologyEconomicsBusinessArtificial intelligenceMicroeconomics

Abstract

fetched live from OpenAlex

Inhibition is considered an essential process to goal pursuit and as a result has become a central construct in many disciplines in psychology and adjacent fields. Despite a century’s worth of debate, however, there is little consensus about what inhibition actually is. We suggest it is time to abandon the concept of inhibition as it currently stands, given that its definition has been problematic. Instead, we propose an alternative framework that suggests inhibition has been improperly operationalized as a process to obtain a goal, when in fact inhibition is the target outcome. To better understand how people can achieve an inhibition goal, we leverage existing process models to further elucidate how people can actively regulate impulses and desires. Although the field has been led astray by classifying inhibition as a process, our current framework seeks to provide greater practical utility to the study of goal pursuit moving forward.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.015
Scholarly communication0.0070.009
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.112
GPT teacher head0.454
Teacher spread0.342 · 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 designTheoretical or conceptual
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
Published2022
Admission routes2
Has abstractyes

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