A research roadmap for evidence-informed policymaking in tourism
Bibliographic record
Abstract
There have been repeated calls from practitioners and academics to make greater use of evidence to improve policymaking. While the tourism and geography literature have occasionally addressed the complex nature of research-practice collaboration, these attempts remain fragmented, somewhat unreflective and scarcely informed by debates in political science. This conceptual paper addresses this gap by identifying tensions in the mainstream literature on evidence-based policymaking and by proposing a future research roadmap for evidence-informed policymaking in tourism. The proposed research agenda stems from four tensions identified in the literature: (i) a lack of consensus on what constitutes evidence, (ii) different understandings of the policymaking process, (iii) the contradiction between evidence and policymaking, and (iv) the politics of evidence and barriers to evidence use. The paper makes several contributions to the discourse on evidence-informed policymaking in tourism geographies. It provides insights from political science discourses to aid our understanding of evidence informed policymaking which will also inform future tourism research. Moreover, the paper explores broader conceptualisations of evidence and examines various forms of its application. It also promotes awareness of diverse philosophical perspectives and emphasises that the effective use of evidence is crucial for achieving evidence-informed policymaking.
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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 | 0.204 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.036 | 0.051 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.034 | 0.033 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".