Knowledge Co-production in Contested Spaces: An Evaluation of the North Slope Borough – Shell Baseline Studies Program
Bibliographic record
Abstract
Supporting the development of trusted and usable science remains a key challenge in contested spaces. This paper evaluates a collaborative research agreement between the North Slope Borough of Alaska and Shell Exploration and Production Company—an agreement that was designed to improve collection of information and management of issues associated with the potential impacts of oil and gas development in the Arctic. The evaluation is based on six categories of knowledge co-production indicators: external factors, inputs, processes, outputs, outcomes, and impacts. Two sources of data were used to assess the indicators: interviews with steering committee members and external science managers (n = 16) and a review of steering committee minutes. Interpretation of the output and outcome indicators suggests that the Baseline Studies Program supported a broad range of research, though there were differences in how groups perceived the relevance and legitimacy of project outcomes. Several input, process, and external variables enabled the co-production of trusted science in an emergent boundary organization and contested space; these variables included governance arrangements, leveraged capacities, and the inclusion of traditional knowledge. Challenges to knowledge co-production on the North Slope include logistics, differences in cultures and decision contexts, and balancing trade-offs among perceived credibility, legitimacy, and relevance. Reinforced lessons learned included providing time to foster trust, developing adaptive governance approaches, and building capacity among scientists to translate community concerns into research questions.
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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 | 0.174 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".