Recovery Planning to Achieve Desired Results: Using Principles of Extension to Create Meaningful Behavioral Changes Linked to Overall Recovery Goals
Bibliographic record
Abstract
Abstract: The Canadian Species at Risk Act requires that recovery strategies be developed under specified time frames for all species and ecosystems that are listed as Threatened, Endangered, or Extirpated. In most cases, recovery teams have been established and charged with developing these recovery strategies. While these groups have tended to focus on the biological aspects of species and ecosystem recovery within their strategies, it is critically important that time and effort is also spent on the human aspects of recovery. The Recovery of Nationally Endangered Wildlife’s (RENEW) guiding principle #1 states that “Species recovery ultimately depends on changing human behavior to allow species to maintain self-sustaining populations. ” Extension, or the process of creating change within a specific audience group, is thus an integral part of species at risk recovery. It is important for recovery teams to firmly integrate these desired changes in human behavior with the biological goals of species and ecosystem recovery. In this paper, we provide recovery teams with an introduction to how they may achieve this integration. Specifically, we introduce the concept of using extension, or nonformal education, to change human behavior. We emphasize that extension should be aimed at specific stakeholders, not the general public, to
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".