Systematic reviews and maps as tools for applying behavioral ecology to management and policy
Bibliographic record
Abstract
Although examples of successful applications of behavioral ecology research to policy and management exist, knowledge generated from such research is in many cases under-utilized by managers and policy makers. On their own, empirical studies and traditional reviews do not offer the robust syntheses that managers and policy makers require to make evidence-based decisions and evidence-informed policy. Similar to the evidence-based revolution in medicine, the application of formal systematic review processes has the potential to invigorate the field of behavioral ecology and accelerate the uptake of behavioral evidence in policy and management. Systematic reviews differ from traditional reviews and meta-analyses in that their methods are peer reviewed and prepublished for maximum transparency, the evidence base is widened to cover work published outside of academic journals, and review findings are formally communicated with stakeholders. This approach can be valuable even when the systematic literature search fails to yield sufficient evidence for a full review or meta-analysis; preparing systematic maps of the existing evidence can highlight deficiencies in the evidence base, thereby directing future research efforts. To standardize the use of systematic evidence syntheses in the field of environmental science, the Collaboration for Environmental Evidence (CEE) created a workflow process to certify the comprehensiveness and repeatability of systematic reviews and maps, and to maximize their objectivity. We argue that the application of CEE guidelines to reviews of applied behavioral interventions will make robust behavioral evidence easily accessible to managers and policy makers to support their decision-making, as well as improve the quality of basic research in behavioral ecology.
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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 | 0.305 | 0.521 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.158 | 0.087 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".