MétaCan
Menu
Back to cohort
Record W7024010917

A Quarter Century of Doping Scandals

2013· other· en· W7024010917 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsMedalFraming (construction)HeadlineStanozololAmateur
DOInot available

Abstract

fetched live from OpenAlex

With the ongoing doping scandals, revelations, and confessions, it was likely that few celebrated this autumn’s significant anniversary in doping history. Twenty-five years ago—September 26, 1988—news broke of the first major doping scandal in the Olympic Games. Canadian sprinter Ben Johnson, who just two days previously had won the 100 meter dash in a world record clip, had tested positive for the banned anabolic steroid stanozolol at the Seoul Olympics. Johnson was neither the first to use prohibited “doping” substances at the Olympics nor the first to get caught. Johnson’s case is notable because it marked the first time a high-profile athlete was unceremoniously stripped of his medal rather than having his results covered up or ignored. Johnson’s case is also useful for framing the ongoing issue with doping in elite sports while providing some insight into the current problem sport faces.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0120.010
Scholarly communication0.0130.009
Open science0.0010.006
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0400.012

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 head0.006
GPT teacher head0.238
Teacher spread0.232 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicMachine Learning in Materials ScienceFrench-language works237,207