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Record W4285042541 · doi:10.1111/capa.12484

Composition, distribution, and change in Canada's federal policy staff

2022· article· en· W4285042541 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Public Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPaceDistribution (mathematics)Government (linguistics)Unit (ring theory)Work (physics)Complement (music)Public policyComposition (language)Public administrationBusinessPublic economicsPolitical scienceEconomicsEconomic growthPsychologyGeographyEngineeringChemistry

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.293
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it