Is Evidence-based Policy Making Really Possible? Reflections for Policymakers and Academics on Making use of Research in the Work of Policy
Bibliographic record
Abstract
Introduction It has been persuasively argued throughout this book and elsewhere (Colebatch 2006; Radin 2000) that mainstream Western accounts of the policymaking process often bear little resemblance to the realities of those who ‘accomplish’ the actual policy work on a daily basis. The discussion that follows examines an area where the disjuncture between outcome-focused accounts of how things ought to work, and interpretive descriptions of how the policy process is experienced is pronounced. It involves the translation of academic research into something that is useful in policy work. In this case, the lack of congruency between process challenges and outcome expectations occurs at all levels of the knowledge-creation process (i.e., in the production, dissemination and utilization of results), and differently by different participants, thereby increasing the level of complexity associated with attempts to understand and resolve this problem. This discussion seeks to illuminate this topic by highlighting the different concerns that researchers and policymakers bring to it. This chapter examines the exchange of knowledge between university researchers and policymakers (such as elected officials, advisors and civil servants), drawing on successful examples of research use in the Canadian context, the insights from organizations dedicated to thinking about these issues, and current empirical findings on this subject. In Canada, the nature of the university-policy nexus is of particular concern because it has been suggested that the federal government presently lacks a strong in-house capacity for conducting research (Howlett 2007; Perl and White 2007), while public funding bodies are trying to induce academic researchers to give a higher priority to the utilization of their research. Nevertheless, I do not address the case of think tanks or the advisory bodies (formal and informal regional, national, and or international) established to provide specific information to governments, which are sites where the transfer and utilization of research also occurs. My discussion assumes that: – creating effective policies for citizens is a goal that policymakers and academic researchers often share; – both researchers and policy workers tend to overlook the challenges for ‘the other’ when thinking about using research in policymaking; – both groups need to think critically about the role they play in perpetuating divisions; – specific strategies can be pursued which may encourage more successful collaborations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".