“We monitor by living here”: community-driven actualization of a social-ecological monitoring program based in the knowledge of Indigenous harvesters
Bibliographic record
Abstract
Researchers and government agencies are increasingly embracing Indigenous knowledge to inform ecological monitoring. However, there are few detailed accounts of designing monitoring methods based in Indigenous knowledge to meet Indigenous objectives. This research details the design of a program initiated by the Gitga’at First Nation to document the knowledge and observations of their harvesters as a contemporary monitoring initiative. We, Gitga’at and academic researchers, first conducted informal interviews with knowledge holders to gauge interest and to establish community objectives. We then convened community meetings and workshops to design methods to document harvesters’ knowledge and observations. We tested and revised these methods (a post-harvest season interview guide, and a logbook to be completed by harvesters) over the course of two harvest seasons. Semi-structured interviews were more successful than the logbooks in meeting multiple community monitoring objectives. However, we were encouraged by younger participants’ suggestions to develop a digital app based on the logbook to encourage future participation. Our work can serve as a guide to other Indigenous peoples and collaborators who wish to leverage the knowledge of their land and (or) sea users, and the methods we develop are available to adapt to other cultural, social-ecological, and political contexts.
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".