The Coasts Under Stress project: a Canadian case study of interdisciplinary methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it