MétaCan
Menu
Back to cohort
Record W2894084273 · doi:10.1177/0146167218801363

Causality in the Theory of Planned Behavior

2018· article· en· W2894084273 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.

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTheory of planned behaviorPsychologyCausality (physics)ReciprocalSocial psychologyCausal modelAttributionField (mathematics)Control (management)Base (topology)PremiseEpistemology

Abstract

fetched live from OpenAlex

The theory of planned behavior proposes that behavior is predicted by behavioral intention which is, in turn, predicted by three base components: attitudes toward the behavior, subjective norms regarding the behavior, and perceived control over the behavior. Implied within this theory is that each of the three base components influence intentions, solely in that direction. However, despite being one of the most widely used theories in many areas of psychology and health sciences, few studies have tested this basic premise. Might causal influence also flow in a reverse-causal direction from intentions back to the base components? This causal sequence was tested and supported by a correlational study, a lab-based experiment, and a quasi-experimental field study. This demonstration of reverse-causal relations from intentions to the base components suggests that the theory of planned behavior should be modified to include reciprocal causal relations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.126
GPT teacher head0.448
Teacher spread0.322 · 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