Harvest-based Monitoring in the Inuvialuit Settlement Region: Steps for Success
Bibliographic record
Abstract
We define harvest-based monitoring as the long-term collection of data or samples from a subsistence harvest in order to reveal, document, and track changes in biophysical resources. Our objective is to describe five practical steps that have guided us over the past two decades during delivery of harvest-based monitoring studies in the Inuvialuit Settlement Region (ISR). Studies have usually been designed to detect (but not necessarily explain) change, to involve local harvesters, and to incorporate indigenous and science-based knowledge. The five steps are to (1) formulate a scientific research or long-term monitoring question that can reasonably be answered by analyzing data from harvests or harvested specimens, (2) design the program according to scientific and indigenous protocols, (3) determine respective partner roles for delivery of the field program, (4) conduct the field work, and (5) analyze data and communicate results. At all steps, it is important to ensure that science and indigenous knowledge partners respect and trust each other’s skills, knowledge, and abilities; that regular communication is fostered; and that provisions are in place to monitor progress. The credible blending of indigenous and scientific views and skills improves the likelihood of ultimately understanding the resource, its habitats, and its inherent ecological relationships.Key words: harvest-based monitoring, Inuvialuit Settlement Region, collaborative research, participatory research
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".