Fostering Evidence-Informed Decision-Making for Protected Areas through the Alberta Parks Social Science Working Group
Bibliographic record
Abstract
Since 2012, the Alberta Parks division in the Province of Alberta, Canada has been engaged in a process of building scientific, research, and evidence-informed capacity and practices across the parks system. Following a series of priority-setting workshops and agreements with the research, Parks management, and local communities, Alberta Parks has adopted a working group approach and subsequent framework, to support the research and decision-making goals of parks and protected areas management, and the research communities. This Social Science Framework is an innovative way to support evidence-informed decision-making in the public sphere by explicitly linking data-specific needs (benchmark data in social, natural, and applied sciences) with both established and emerging policy and research priorities. It is also a way to situate those needs within a broader goal of inter-organizational collaboration. This paper presents the background and developmental context to the framework, and its structure and desired functionality. The paper concludes with an assessment of the anticipated benefits and potential liabilities of this direction for linking academic and policy agents and organizations in a more formalized structure for environmental policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.294 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.039 | 0.011 |
| Open science | 0.007 | 0.047 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".