Use of systematic literature reviews in Canadian government departments: Where do we need to go?
Bibliographic record
Abstract
Abstract The article reports on ongoing reflections on how to improve the structures and processes by which relevant research findings produced outside Canadian government departments (for example, in universities, think tanks or other research institutions) can be more effectively found, assessed for potential biases, synthesized and disseminated to provide support to government analysts, advisers and decision makers. The focus is on how to structure and routinize research use by government analysts and advisers within a large Canadian department that has a strategic research directorate and many program divisions. Our starting point is the current situation where literature reviews that are produced and used by government analysts and advisers do not correspond to the systematic review standards. We discuss four alternative models. In two models, the whole production process would be controlled from within the department. In the other two models, external actors such as university‐based teams or independent non‐governmental evidence centres would carry the leadership in producing those reviews.
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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 | 0.618 | 0.765 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.036 | 0.064 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.047 | 0.026 |
| Open science | 0.016 | 0.017 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".