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Record W2906573979 · doi:10.12689/jmep.2014.201

Putting Reversal Theory’s Model of Four Domains of Experience in the Hot Seat

2014· article· en· W2906573979 on OpenAlexaff
Étienne Mullet, Lonzozou Kpanake, Ornheilia Zounon, Myriam Guedj, Marı́a Teresa Muñoz Sastre

Bibliographic record

VenueJournal of Motivation Emotion and Personality Reversal Theory Studies · 2014
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsRubricPsychologyDomain (mathematical analysis)Set (abstract data type)Social psychologyDomain theoryMental healthCognitive psychologyDevelopmental psychologyMathematicsComputer scienceMathematics educationPsychotherapist

Abstract

fetched live from OpenAlex

We present, in a synthetic way, the main findings from a series of ten studies in the domain of health psychology. All of these studies have inventoried motives to perform or not perform a given health-related behavior (e.g., consulting a physician) without postulating any a priori motivational structure. As a result, the whole set of studies allowed testing the capacity of reversal theory's model of four domains of experience to account for motivational data gathered in different settings but on the common ground of health-related behavior. From five to ten factors were found in each study, and all these factors were classifiable in one or other of the twelve categories offered by the structure of four domains of experience when transactions and relationships were considered in combination. All factors posited by reversal theory were found except one; the only factor that was not found at least once was of the pro-autic kind. In some cases, two factors of motives had to be classified under the same rubric, which led to the suggestion that the relationship domain may perhaps be extended. Overall, our findings suggest that the four-domain model, and its associated ten mental states, encompass and surpass previous theories of human motivation.

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 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.133
GPT teacher head0.361
Teacher spread0.228 · 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 teacher head, 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

Citations8
Published2014
Admission routes1
Has abstractyes

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