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Record W4243956068 · doi:10.1057/9780230367463_1

Introduction

2012· book-chapter· en· W4243956068 on OpenAlexaff
Helen Jefferson Lenskyj, Stephen Wagg

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsSkepticismState (computer science)Media studiesSympathyPolitical scienceClassicsHistoryArtSociologyLawPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Scholarly writing on the Olympic Games has diversified quite dramatically in the last 25 years or so. Time was when this writing was mostly done by people (invariably male) who might be characterised as in-house - that’s to say, they were close to and broadly in sympathy with the Olympic movement and its aims. Their work tended to explore in detail the history of the modern Olympics (and particularly the life and work of their chief architect, Pierre de Coubertin) or to ruminate on its changing philosophy; it would be aired at conferences and in specialist journals such as Olympika or the Olympic Review ; it invariably celebrated the Olympics and its writers themselves would often be Olympic officials in their respective countries. Even those critical of some Olympic orthodoxies have nevertheless remained supportive of the modern Olympics. David Young, Professor of Classics at Florida State University, is a case in point. A veteran sceptic of Coubertin’s role and of his claims to have launched an authentic revival of the original Games in ancient Greece (see Mark Golden’s chapter in this book), he nevertheless gave a speech at the Greek Embassy in Washington DC in March 2001, welcoming the award of the 2004 Summer Games to Athens. 1 In general, discussion would be concerned with the what of past Olympic policy and politicking and the whither of Olympic practice and values; it seldom interrogated the Olympics as a social and political phenomenon or touched the question of whether the Olympics were ‘a good thing’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.570
Threshold uncertainty score0.613

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0080.005
Open science0.0030.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.5700.399

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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooks→Same topicSport and Mega-Event Impacts→French-language works237,207→