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Record W3136340384 · doi:10.1080/09523367.2020.1866474

The Winter Olympics: A Century of Games on Ice and Snow

2020· article· en· W3136340384 on OpenAlexaff
Heather L. Dichter, Sarah Teetzel

Bibliographic record

VenueThe International Journal of the History of Sport · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGlobeAmbush marketingHistoryEvent (particle physics)GeographyAthletesPolitical scienceMedia studiesSociologyPsychology

Abstract

fetched live from OpenAlex

The Olympic Winter Games, although younger than their summer counterpart, nonetheless have a long and storied history. From the first iteration in Chamonix, France, in 1924, through the first off-set games in Lillehammer, Norway, in 1994, to the most recent edition in PyeongChang, South Korea, the Olympic Winter Games are truly a mega-event. This introduction to a winter Olympics anthology considers the nearly one-hundred years of the Olympic Winter Games, including its growth in athletes and events, and the broader impact of that expansion. The sports on the winter Olympic program are at least not easy and in many places largely impossible to practice in at least half of the world’s countries. These challenges make the Olympic Winter Games a compelling event to study because its sports are not universally practiced by people in every country across the globe. How the Olympic Winter Games have grown in scale and size from an event for northern and central Europeans, Americans, and Canadians to include participants from countries that lack snow and ice is itself a testament to the global power of the Olympic brand. The essays in the winter games collection take a broad view of topics related to the Olympic Winter Games, including new approaches to understanding the history and historical significance of athlete eligibility and inclusion, youth culture, demonstration sports, and legacy.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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