Accompanying Data for "Collaboration and Engagement with Decision-Makers Needed to Reduce Evidence Complacency in Wildlife Management"
Bibliographic record
Abstract
Accompanying Data for "Collaboration and Engagement with Decision-Makers Needed to Reduce Evidence Complacency in Wildlife Management". Displayed are each relationship of evidence flow produced in each fuzzy cognitive map from" 1) The Freshwater Fisheries Society of BC (FFSBC; https://www.gofishbc.com). 2) Natural resource management branches of First Nations Indigenous governments (n=2). 3) Headquarters (i.e., ‘Branch’) of the BC Ministry of Forests, Lands and Natural Resource Operations and Rural Development (FLNRORD)*). 4) Regional offices of FLNRORD (i.e., ‘Regions’). *At the time of research this was the Ministry name, but as of March 2022 has changed to ‘the BC Ministry of ‘Lands, Water and Resource Stewardship’. Displayed for each relationship (arc) are the origin and destination of evidence flow along with the following ratings: 1. Amount of information flow 2. Rate of information flow 3. Reliability of the information flow (i.e., signal to noise ratio) which is comprised of a composite index: a. credibility and reliability (i.e., trust, faith, and confidence in the information) b. distortion (i.e., potential misuse or bias of the information) c. hackability (generativity) (i.e., the degree to which the information lends itself to tinkering, modification, exploitation, flexion) d. availability e. political-ness
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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.772 | 0.371 |
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".