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Record W2140421868

Happy and Unhappy Competitors: What Makes the Difference?

2009· article· en· W2140421868 on OpenAlexaboutno aff
Márta Fülöp

Bibliographic record

VenueUniversity of Zagreb University Computing Centre (SRCE) · 2009
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisPsychologyEconomicsManagement
DOInot available

Abstract

fetched live from OpenAlex

Interpersonal competition is present in all arenas of our life, i.e. within the family, in school, among peers, in the workplace, and in the sports ground.Competition can be an immensely joyful, exciting, and motivating experience that contributes to goal attainment, self-evaluation, development and improvement of the individual, the competing parties, the group and the society.However, it can also be an anxiety provoking, stressful, and exhausting negative experience that leads to interpersonal conflicts and has destructive consequences individually, to the group and ultimately to the society.Competition can be a friendly process in which the competitive parties mutually motivate and improve each other, but can also be a desperate fight full of aggression among the competitors who consider each other enemy.The result of competition can be winning or losing.Winning typically evokes positive emotions like happiness, satisfaction, and pride, but sometimes negative emotions emerge like guilt or embarrassment.Losing, as a potential result of competition, may result in sadness, disappointment, frustration, anger, shame, but can have positive consequences like learning about the self, realizing strengths and weaknesses and increased motivation for the future.There is not "one" competitive process.Competition can take qualitatively different forms and patterns that are determined by individual, situational and cultural factors.The paper will examine the factors that can be decisive in this respect: i.e., the characteristics of the competitive situation and the characteristics of the competing person.These situational and personality requirements will be further examined from a cultural perspective, taking examples from East-Asia (Japan), from North America (Canada) and from Europe (Hungary).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.203
Teacher spread0.194 · 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 designQualitative
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

Citations57
Published2009
Admission routes1
Has abstractyes

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