Policy Capacity and Incapacity in Canada's Federal Government
Bibliographic record
Abstract
Governments, world-wide, are preoccupied with avoiding policy failure. A high level of policy capacity is considered one indicator of addressing this issue. Canada is typical of most countries where policy-related work tends to be centralized within its national capital city (Ottawa). There have been criticisms that on-the-ground perspectives are not conceded in policy decisions. Given the vast size and the decentralization of power, very little research has been dedicated to policy work conducted in its regions and whether it contributes to strengthening policy capacity. This article employs eight key hypotheses about contribution of Canadian regionally-based federal policy work to policy capacity based upon data derived from a national survey. A structural equation model (LISREL) is used to present the results. We find that regional-based policy work currently does little to enhance policy capacity. Policy work is divided along two distinct functional lines: traditional policy analysis and ‘street-level’ bureaucracy. The more engaging policy analysts belong to formal policy units which are a critical aspect of stronger policy capacity. The second factor contributing to policy capacity were attitudes towards the larger political arena.
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.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".