Co-producing uncertainty in public science: The case of genomic selection in forestry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it