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Record W1989363949 · doi:10.1177/1012690205052163

The Disappointment Games

2005· article· en· W1989363949 on OpenAlexafffundabout
Graham Knight, Margaret MacNeill, Peter Donnelly

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of TorontoMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Waikato
KeywordsDisappointmentNormativeContingencyNarrativeSociologyPositive economicsRelation (database)Rhetorical questionPsychologyEpistemologySocial psychologyEconomicsPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This article provides a comparative analysis of news narratives of ‘disappointment’ in Canada and New Zealand in response to the 2000 Olympics. The theoretical framework draws on Luhmann’s distinction between cognitive and normative orientations to expectations, contingency, and disappointment. The analysis examines how disappointment was thematized similarly as a decline in relation to past performance, but explained somewhat differently in the two countries. In New Zealand, disappointment was explained in more normatively inflected terms. Although various causal factors were mentioned, the explanatory frame was dominated by claims that athletes lacked a competitive attitude and the ‘will to win’, and this was generalized to New Zealand society and the educational system in particular as indicative of a broader loss of moral values. The Canadian response, on the other hand, was framed in more cognitively oriented terms. Athlete blaming was quickly dismissed as misplaced, and attention was directed to the lack of government funding and organizational problems in the ‘sport system’ as the principal reasons for disappointing results. In both cases, however, these explanations of disappointment were not fully exclusive of one another; each continued to contain subsidiary elements of the other, which indicates how the normative and cognitive remain mutually implicated.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.494

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 science0.0010.000
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 head0.042
GPT teacher head0.380
Teacher spread0.339 · 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 designNot applicable
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

Citations36
Published2005
Admission routes3
Has abstractyes

Explore more

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