Reviving the past to protect the future: developing a social-ecological clam garden site selection model
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
The tightly coupled social-ecological nature of fisheries calls for science and management to work in tandem across knowledge systems to address the complex issues impacting fisheries productivity and associated benefit flows. However, the pragmatics of how to accomplish this in an equitable manner are rarely discussed. In this paper we provide a real-world example of how to effectively and meaningfully weave ecological and social sciences with diverse knowledge and ways of knowing in order to revive an ancient Indigenous aquaculture practice to address climate-related resource management and community health concerns. Specifically, we present the detailed steps of our transdisciplinary clam garden site selection process: 1) create Technical Advisory Group, 2) develop initial exclusion map, 3) collect ecological data and conduct multi-criteria decision analysis, 4) collect socio-cultural data, and 5) select a site. Our methodical, stepwise framework included collaborative management through community participation and decision-making, and utilization of multiple perspectives. This resulted in a transparent, inclusive process that garnered community support and increased the likelihood for successful implementation. Our work is specific to the Swinomish Indian Tribal Community; however, the process can be adapted to address the place-based needs and values of other coastal communities.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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