Bibliographic record
Abstract
Risk communication is a foundation of the practice of public health.It is traditionally based on a carefully considered epidemiological computation of the likelihood of experiencing a condition given the presence of a particular exposure or behaviour.The extent to which numerical precision is important in such communication is a function of the availability of good statistics, the ability of the target audience to appreciate the meaning of the statistics, and the emotional heft represented by the chosen statistic.There is an inherent danger, however, in overweighting the latter consideration at the expense of the former two.When emotional impact and behavioural change become goals to the exclusion of complete scientific credibility, we risk brushing against the realm of propaganda in service of unexplored unconscious societal moralism.In this era of heightened distrust of state authority, it behooves public health communication to avoid the suggestion of data misrepresentation in service of behaviour change, regardless of how socially desirable that change might be.
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 | 0.022 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.105 | 0.028 |
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".