Bibliographic record
Abstract
The complex socio-environmental issues faced by society - including climate change, resource management, and fostering resiliency in landscapes that intermix human and natural features - are difficult challenges that demand contextually appropriate evidence-based interventions. Institutional arrangements for providing scientific advice range from individual science advisors to large scientific committees or advisory councils, with a great deal of variation in their formal and informal structures. Regardless of the structuring of advisors, however, these arrangements face a common challenge: being required to speak to a wide range of issues in a time-sensitive manner, each of which has extensive stakeholder communities, deep disciplinary knowledge, and many complicating attributes. It is argued that creating a formally associated, supporting boundary organization that is tasked with supporting the advisory functions can help to resolve this challenge and improve the overall quality of advice offered. Using a case study - the California Ocean Science Trust and its advice on coastal and ocean management issues - it is argued that boundary organizations can help science advisors maintain links with disparate stakeholder communities, adjudicate between competing forms of expertise, help to provide nuance in grappling with the tensions between science and politics, and support an "honest broker" advising function.
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 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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".