Comfort in big numbers: Does over-estimation of doping prevalence in others indicate self-involvement?
Bibliographic record
Abstract
BACKGROUND: The 'False Consensus Effect' (FCE), by which people perceive their own actions as relatively common behaviour, might be exploited to gauge whether a person engages in controversial behaviour, such as performance enhancing drug (PED) use. HYPOTHESIS: It is assumed that people's own behaviour, owing to the FCE, affects their estimation of the prevalence of that behaviour. It is further hypothesised that a person's estimate of PED population use is a reliable indicator of the doping behaviour of that person, in lieu of self-reports. TESTING THE HYPOTHESIS: Over- or underestimation is calculated from investigating known groups (i.e. users vs. non-users), using a short questionnaire, and a known prevalence rate from official reports or sample evidence. It is proposed that sample evidence from self-reported behaviour should be verified using objective biochemical analyses.In order to find proofs of concept for the existence of false consensus, a pilot study was conducted. Data were collected among competitive UK student-athletes (n = 124) using a web-based anonymous questionnaire. User (n = 9) vs. non-user (n = 76) groups were established using self-reported information on doping use and intention to use PEDs in hypothetical situations. Observed differences in the mean estimation of doping made by the user group exceeded the estimation made by the non-user group (35.11% vs. 15.34% for general doping and 34.25% vs. 26.30% in hypothetical situations, respectively), thus providing preliminary evidence in support of the FCE concept in relation to doping. IMPLICATIONS OF THE HYPOTHESIS: The presence of the FCE in estimating doping prevalence or behaviour in others suggests that the FCE based approach may be an avenue for developing an indirect self-report mechanism for PED use behaviour. The method may be successfully adapted to the estimation of prevalence of behaviours where direct self-reports are assumed to be distorted by socially desirable responding. Thus this method can enhance available information on socially undesirable, health compromising behaviour (i.e. PED use) for policy makers and healthcare professionals. The importance of the method lies in its usefulness in epidemiological studies, not in individual assessments.
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 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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".