Comparing sub-national policy workers in Canada and the Czech Republic: Who are they, what they do, and why it matters?
Bibliographic record
Abstract
Abstract This article compares profiles and policy-related activities of policy workers (PWs) in thirteen Canadian provinces and territories with PWs in the Czech Republic regions. Canadian data come from 13 separate surveys conducted in provinces and territories in late 2008 and early 2009 (N = 1357). The Czech data are from analogical large-scale survey carried out at the end of 2012 (N = 783). First, the paper compares basic characteristics of Canadian and Czech PWs. In the two countries the proportion of men and women is similar and PWs are equally highly educated. Examining other characteristics, however, reveals substantial differences. When compared with the Czech PWs, Canadian PWs tend to be older, more often having social science educational backgrounds, more frequently recruited from academia, stay in a single organization for a shorter period of time and anticipate staying in their current position for only a short time. Second, a comparison of policy-related work activities discerns three basic clusters of policy tasks: policy analysis work, evidence-based work, and consulting/briefing. Canadian PWs are much more involved in evidence-based work, especially in evaluation and policy research. They also deal more with policy analysis activities such as identification of policy issues and options. In contrast, Czech PWs are more engaged in consulting with the public and briefing managers and decision-makers. The article concludes with implications for further research and theory building.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".