Composition, distribution, and change in Canada's federal policy staff
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Using a decade of administrative data from the Government of Canada, we provide fresh analysis of the composition and distribution of staff most formally associated with policy work, the Economics and Social Science (EC) classification. Comparative analysis across unit levels including “ministerial departments” and central agencies, as well as non‐standard organizations support but clarify the nature of the uneven distribution of policy analytical capacity across government. We demonstrate a dramatic increase in not only the overall complement of EC staff over time, particularly since 2017, but also significant growth at senior levels while junior EC staff have remained stable or declined. The findings also point to new dynamics related to the pace, orientation, and distribution of policy analytical capacity as governments gain, lose, and exercise that capacity often in the face of tough choices about how, where, and when to deploy policy resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it