Discussion on IPM 48: Statistics Education for Media Reports
Bibliographic record
Abstract
In interpreting the title of this session in the light of the three papers presented, I have defined “for ” as “to prepare for. ” The next point of clarification is, “for whom ” is the preparation for media reports necessary? And the final point of clarification relates to the “type ” of media reports referred to in the title. In clarifying “for whom, ” two of the papers (Gal and Snell) focus on students, at school or university, as the people for whom statistical education is needed. Both authors see the aim as the preparation of citizens who can participate meaningfully in society. Gal’s thrust is more generally based in statistical understanding, with suggestions for educators, whereas Snell focuses specifically on a university course teaching about chance. The third paper (Podehl) takes a different perspective, looking at journalists, in particular those reporting on information from Statistics Canada, the country’s National Statistical Office. The requirements of these people are similar to the school and university students in terms of statistical literacy but the material with which Podehl works is much more restricted than the other two authors. Turning to the “types ” of media reports, two of the papers (Gal and Podehl) focus on press releases from statistics agencies, whereas the third (Snell) considers secondary media reports
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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 | 0.097 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.034 | 0.030 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.060 | 0.060 |
| Insufficient payload (model declined to judge) | 0.079 | 0.027 |
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".