The Coasts Under Stress project: a Canadian case study of interdisciplinary methodology
Bibliographic record
Abstract
SUMMARY Interdisciplinary research requires scholars to learn by doing, and thus interdisciplinary work will be constantly undergoing development. This paper reviews how a large truly integrated interdisciplinary research team capable of handling complex interdependent social and environmental issues was created, developed and managed. The Canadian Coasts Under Stress bicoastal research project (CUS) constitutes a case study, aimed at providing a detailed analysis of a successful relatively ‘mature’ template for interdisciplinary team research that can be transferred to other teams and other research problems. CUS was created to address coastal social-ecological stress, and it uncovered linkages (‘pathways’) between the main drivers of social-ecological health in both human and environmental communities. In so doing, the team produced a comprehensive new way to understand restructuring and its impact on social-ecological health. In organizational terms, the team was divided into two coastal sub-teams (east and west) and five main research components that were reflected in the team logo as the arms of a seastar. To achieve integration of all components and subcomponents, a methodology for research construction and integration was employed that operated in tandem with the methodologies employed in the various subcomponents. Team members shared their vision of what they wished to achieve and meetings were facilitated in a variety of ways such that cross-fertilization and discussion were ongoing, and team members always knew exactly where their work fitted into the greater whole. In the process, significant student training occurred, and the challenge of equitable publication processes were met such that the output of the team achieved both disciplinary rigour and interdisciplinary understanding.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.053 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".