Assessing policy analytical capacity in contemporary governments: New measures and metrics
Bibliographic record
Abstract
Abstract Assessing the policy analytical capacity (PAC) of governments has suffered in the past from the anecdotal nature of most studies, leading to different evaluations of specific analytical activities and of the overall competences and capacities of governments as a whole. What is needed to advance the field is a set of metrics that can generate insights into the capabilities of different units and how changes to their and overall government capacity develop over time. Focusing on this component of policy capacity, we map and measure the distribution of policy professionals in the provincial, territorial, and federal governments in Canada. Our measures are tested against two major findings regarding PAC: first that variation among governmental PAC varies by size of the civil service, with smaller jurisdictions likely to have less capacity, and second, that concentration of professionals in specific issue areas underscores that area's political and/or policy salience to the government concerned. Both measures prove robust in assessing Canadian government activities in these areas. Points for practitioners Policy capacity is acknowledged as a significant perquisite for policy success. While some general frameworks exist highlighting policy relevant competences and capabilities important to policy success, how to measure these remains under‐investigated. Focusing on policy analytical capacity, this paper draws on the literature on policy professionals to develop two measures of this component of policy capacity linked to the extent to which an agency focuses on analysis and the proportion of their staff who work on the subject compared to other agencies. The measures are deployed in an illustrative case of Canada and Canadian governments at the territorial, provincial, and federal level which confirms their utility and robustness as indicators of the different levels of analytical capacity different agencies employ.
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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 | 0.020 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.041 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".