Statistics, Social Relevance, Policy, and Analysis: Why They Are Bound Together
Bibliographic record
Abstract
From mid-1972 to early 1975, Sylvia Ostry was chief statistician of Canada.Statistical offices are not known to respond quickly to new challenges, for continuity is one of the elements of their trade.They respond even more slowly or not at all to possible changes in mission.The Canadian statistical office was no exception to this rule, and Ostry's tenure in the organization's top post was short.However, she managed to sow the seeds of something that was to affect the organization's posture, mission, and importance within the array of federal institutions many years later: the need to be policy-relevant and to ensure that the Canadian statistical office stayed relevant by maintaining a well-developed analytical capacity.This chapter refers mostly to the role of such a capacity in an organization created to produce official statistics.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".