Lessons for introducing stakeholders to environmental evidence synthesis
Bibliographic record
Abstract
Involving stakeholders in systematic reviews is common practice and is advised in the Collaboration for Environmental Evidence (CEE) Guidelines (v.4.2). Frameworks for engaging stakeholders exist and should be used; however, there are additional lessons to be learned in a country, or region where evidence-based environmental management is an emerging paradigm. Based on our experience working with Canadian governmental institutions, we provide five lessons that we have learned while introducing stakeholders to the CEE systematic review (hereafter SR) process. These lessons are: (1) Advocate for a systematic review with broad geographical scope and target audience; (2) Control stakeholder mission-creep; (3) Establish a mutually beneficial timeline; (4) Reduce the potential of biased targeted searches; and (5) Manage stakeholder expectations. By incorporating these lessons into existing frameworks, we hope to make the introduction of SRs to stakeholders more efficient to conserve resources and maintain long-lasting, productive relationships between the review team and stakeholders.
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 | 0.686 | 0.767 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.037 | 0.066 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.031 | 0.056 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".