The Credibility Chasm in Policy Research from Academics, Think Tanks, and Advocacy Organizations
Bibliographic record
Abstract
How do key policy professionals inside government view various sources of policy research? Are there systematic differences in the perceptions of the quality and credibility of research derived from different sources? This is a replication of and expansion on Doberstein (2017), which presented a randomized controlled survey experiment using policy analysts to systematically test the source effects of policy research. Doberstein's experimental findings provide evidence for the hypothesis that academic research is perceived to be substantially more credible to government policy analysts than think tank or advocacy organization research, regardless of its content, and that sources perceived as more ideological are much less credible. This study replicates that experiment in three additional Canadian provincial governments to verify whether the relationship found in the original study persists in a larger sample and in conjunction with further randomization procedures. This study corroborates the original study's findings, confirming that external policy advice systems are subject to powerful heuristics that bureaucrats use to sift through evidence and advice.
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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 | 0.262 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| 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; 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".