Bibliographic record
Abstract
Abstract Rice, J. C. 2011. Advocacy science and fisheries decision-making. – ICES Journal of Marine Science, 68: 2007–2012. Science advice is supposed to meet idealistic standards for objectivity, impartiality, and lack of bias. Acknowledging that science advisors are imperfect at meeting those standards, they nonetheless need to strive to produce sound, non-partisan advice, because of the privileged accountability given to science advice in decision-making. When science advisors cease to strive for those ideals and promote advocacy science, such advice loses the right to that privileged position. There are temptations to shape science advice by using information that “strengthens” the conservation case selectively. Giving in to such temptation, however, dooms the advice; science advice becomes viewed as expressions of the biases of those who provide it rather than reflecting the information on which the advice is based. Everyone, including the ecosystems, loses. There are ways to increase the impact of science advice on decision-making that do not involve perverting science advice into advocacy: peer review by diverse experts, integrating advice on ecological, economic, and social information and outcomes, and focusing advisory approaches on risks, costs, and trade-offs of different types of management error. These approaches allow the science experts to be active, informed participants in the governance processes to aid sound decision-making, not to press for preselected outcomes. Everyone, including the ecosystems, wins.
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.
How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".