The challenges of harmonising anti-doping policy implementation
Bibliographic record
Abstract
The policy-implementation gap conceptualises how policy intentions and outcomes often differ due to a failure to consider the realities of implementation. The World Anti-Doping Agency (WADA) directs Olympic anti-doping policy, seeking to harmonise anti-doping policy globally; however, the realisation of consistent implementation has proven challenging. A major cause of inconsistent policy implementation is inter-signatory variation, but the mechanisms of variation are poorly understood. WADA provides an excellent example to explore why policy gaps occur in international sport governance. Consequently, we aimed to analyse the different types of inter-signatory variation in anti-doping policy and identify practical solutions to address inter-signatory variation in anti-doping. Data were collected from the Regional Anti-Doping Programme (RADO), a group of organisations tasked with increasing the capacity of NADOs globally. Semi-structured interviews were conducted with 22 RADO staff and board members who were sampled as key informants to discuss how inter-signatory variation affects anti-doping policy compliance. Following reflexive thematic analysis, we identified four thematic categories explaining inter-signatory variation in anti-doping implementation: (1) socio-geographic, (2) political, (3) organisational, and (4) human resources. Based on our analysis, we theorise why the policy-implementation gap occurs and provide recommendations to improve anti-doping policy implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.155 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".