Peering into the black box of government policy work: The challenge of governance and policy capacity
Bibliographic record
Abstract
There have been calls for more diffused policy advisory systems where a plurality of actors, particularly actors from non-governmental organizations (NGOs), engage with government in deliberating policy interventions to address collective problems. Previous research has found that government-based policy workers tend to have low levels of interaction with outside actors. However, very little is understood about the nature of these interactions. To shed light on this important relationship, a multi-regression structural equation model examines the nature of government-based policy work across three Canadian provinces. From an online survey of 603 Canadian provincial government policy workers, we develop six hypotheses that focus on the drivers of policy capacity and their degree of interaction with non-governmental organizations. The results revealed that increased interaction by the respondents with stakeholders was an important determinant for inviting stakeholders to policy discussions and led to increased perceptions of policy capacity. However, the ongoing trend of politicization in policy work had a dampening impact on overall policy capacity. More importantly, it appears that undertaking more evidence-based policy work did not lead to a greater policy capacity perception or interaction with stakeholder groups. The survey design and model development have the potential to be replicated in other jurisdictions.
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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 | 0.052 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.052 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".