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Record W1967533149 · doi:10.1080/19406940.2010.507210

Surveillance and control in sport: a sociologist looks at the WADA whereabouts system

2010· article· en· W1967533149 on OpenAlexfundno aff
Ivan Waddington

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsContext (archaeology)LegitimacyAgency (philosophy)SociologyGovernmentalityEliteAthletesControl (management)DemocracyPublic relationsLaw and economicsPolitical scienceLawPoliticsManagementSocial science

Abstract

fetched live from OpenAlex

This paper draws upon the sociology of Norbert Elias to examine some central aspects of the whereabouts system introduced by the World Anti-Doping Agency (WADA) as part of its anti-doping policies. More specifically, the paper aims to: (1) locate the whereabouts system within the context of broader social processes, including changing practices and ideas concerning surveillance and control, personal liberty, privacy and democracy; (2) examine the impact of the introduction of the whereabouts system on the relationship between elite athletes and WADA; and (3) examine some of the difficulties in developing and implementing anti-doping policy. In relation to the latter, it is suggested that the introduction of the whereabouts policy has had a number of unplanned consequences which, from WADA's perspective, will almost certainly be seen as unwelcome: the alienation of large numbers of athletes, whose cooperation is essential if the system is to operate smoothly and efficiently; the deteriorating relationship with other key organizations such as the EU; the emergence of a challenge, led by the European Elite Athletes Association, to the legitimacy of decision-making processes within WADA; and finally, the uneven application of the whereabouts requirements which has led to the creation of what many athletes see as a new form of unfairness.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.996

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.317
Teacher spread0.301 · 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 designObservational
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

Citations52
Published2010
Admission routes1
Has abstractyes

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