Seeds of change? Seed transfer governance in British Columbia: insights from history
Bibliographic record
Abstract
Tree seed transfer is critical to effective reforestation programs, and exploring its policy roots provides insights to understand future, and potentially controversial, actions like assisted migration. We offer a historical overview of seed transfer governance in British Columbia, Canada, by applying analytics from the policy change and knowledge co-production literatures. Based on document analysis and semi-structured interviews with key informants, we trace governance attributes to examine how and why policies have changed (or not) over time. We reveal a paradigmatic shift in seed transfer governance, culminating in a climate-based seed transfer system — informed largely by genetic knowledge — that emerged through a policy window opening. In contrast, governance processes remained relatively unchanged in practice, including the disproportionately influential role of the forest industry in policy-making. These insights shed light on the legacies of a government–industry policy coalition that influence underlying seed transfer objectives (i.e., forest productivity), and help to explain the ongoing dominance of particular knowledge forms used to inform policy. We highlight the need for increased contributions from a wider range of expertise, stakeholders, and rights holders in developing seed transfer policies for future forests.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".