Are we ready to be wrong? Extended peer community for quality science-advice in uncertainty
Bibliographic record
Abstract
This paper delves into the challenges of achieving inclusion within science-advice institutions, particularly focusing on the International Council for the Exploration of the Sea (ICES). It explores the normative and practical implications of broadening the epistemic space to incorporate diverse ways of knowing in uncertain contexts. Traditional science-advice often relies on strict quantification and institutionalized expertise, limiting the recognition of alternative perspectives. The study proposes an alternative view rooted in post-normal science, advocating for the adoption of an extended peer community model. Despite ICES's efforts to enhance stakeholder engagement through its Stakeholder Engagement Strategy, gaps remain in effectively valuing epistemic diversity. By analyzing a historical case involving the revision of fishing quotas for Northeast Atlantic mackerel, the paper illustrates the limitations of strict quantification in addressing complex and uncertain problems. It recommends a participatory approach informed by post-normal science principles and incorporates the concept of “epistemic injustice” in Miranda Fricker’s work (Fricker, 2003, 2007) to the discussion to underscore the ethical imperative of inclusive decision-making. Ultimately, the paper advocates for post-normal science approaches to better address contemporary challenges in science-advice institutions when the problem is deeply uncertain and complex. • Post-normal science approach advocated for inclusive decision-making in science-advice institutions. • Extended peer community model proposed to incorporate diverse ways of knowing in uncertain contexts. • ICES's efforts to enhance stakeholder engagement highlighted, but gaps in valuing epistemic diversity remain. • Introduction of Fricker’s concept “epistemic injustice” to frame the ethical imperative of inclusive decision-making. • Case study illustrates limitations of strict quantification in addressing complex, uncertain problems.
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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 | 0.048 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".