Creating a quiet buzz: opportunities and challenges for meaningful participation of boreal forest apiarists in the science-policy interface for biodiversity and ecosystem services
Bibliographic record
Abstract
Boreal apiary and beekeeping are characteristic of Eastern Europe and are passed down from generation to generation. Boreal apiary relies on ecological and cultural knowledge to protect honeybee diversity, change forestry practices, and ensure sustainable livelihoods. In this article I discuss the participation of Polish apiarists in the science-policy interface for biodiversity and ecosystem services IPBES Values Assessment and the Indigenous and Local Knowledge (ILK) meetings. Although the process permitted recognition of boreal apiary at a global stage, several obstacles, including essentialist typologies and Anglophone scientific discourses, convoluted meaningful participation. By elaborating on the apiarists’ ecocultural knowledge, I summarize the IPBES ILK Process, the limitations of active engagement by the apiarists, and offer considerations to make science-policy interface ILK engagement more inclusive for culture-custodians.
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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 | 0.031 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.033 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.028 |
| Research integrity | 0.004 | 0.011 |
| 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".