Bibliographic record
Abstract
As a rule, it's fair to say that journalists and statisticians have little in common. Yet, journalists and national statistical agencies are virtually inseparable. Why? Because the general public is an important audience for national statistical agencies and the news media are a powerful tool for reaching this audience. Most journalists are uncomfortable with numbers: many are unable to calculate a percentage increase; many more would find it difficult to explain the difference between a percentage decline and a percentage point decline. Most probably find data boring. A journalist with specialized knowledge of statistics is a rarity. Statistics Canada, like most national statistical agencies, places great importance on communicating with the media. Our challenge is twofold: to engage the interest of journalists in our data and surreptitiously raise the level of their statistical literacy and to engage the interest of our statisticians in presenting statistics in a manner which the journalist, as a layperson, can understand. The paper will outline the various approaches that Statistics Canada has taken to meet this twofold challenge and will discuss our experiences in educating both journalists and statisticians to tell the story behind the numbers.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".