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Making People's Life Most Worth Living: On the Importance of Passion for Positive Psychology

2013· article· en· W1981810246 on OpenAlexaff
Robert J. Vallerand, Jérémie Verner‐Filion

Bibliographic record

VenueTerapia psicológica · 2013
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPassionPositive psychologyPsychologySocial psychologyPsychoanalysis

Abstract

fetched live from OpenAlex

The purpose of the present paper is to present the Dualistic Model of Passion (Vallerand et al., 2003) and show its importance for positive psychology.Passion is defined as a strong inclination toward a self-defining activity that people like (or love), find important, and in which they invest time and energy.Furthermore, two types of passion (harmonious and obsessive) are proposed.Harmonious passion leads people to choose to engage in the activity that they love.Conversely, obsessive passion creates an internal pressure to engage in the beloved activity.Harmonious passion is hypothesized to lead to more adaptive outcomes than obsessive passion.Results of several studies reveal that passion matters with respect to a number of outcomes deemed important for positive psychology such as flow and positive emotions, psychological well-being, physical health, relationships, and performance.Passion can indeed make people's lives worth living to the extent that it is harmonious in nature. Key words : passion,

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.010
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.343
Teacher spread0.292 · 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

Citations93
Published2013
Admission routes1
Has abstractyes

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