Co-producing uncertainty in public science: The case of genomic selection in forestry
Bibliographic record
Abstract
Co-production can inform analysis and communication of the uncertainties associated with novel forms of science and technology. Genomic selection-a relatively novel management tool consisting of predictive modeling based on associations between genetic and phenotypic data-holds many unknowns, particularly when used as a climate adaptation strategy. Approaching genomic selection as an example of public science, we examined beliefs about uncertainty and public engagement in a community of forest research professionals. Findings show broad-ranging approaches to uncertainty, alongside a prevalence of deficit accounts of public engagement. Even with broad acknowledgment of a range of uncertainties, forestry experts nonetheless relied on statistical, quantitative methods to manage uncertainties, in ways that overshadowed discussions about ignorance, indeterminacy, and ambiguity. Social scientists can enhance the communication of uncertainty in public science by making apparent expert-based assumptions about knowledge and intended audiences.
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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 | 0.111 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.069 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".