The impact of climate change on the well‐being and lifestyle of a First Nation community in the western James Bay region
Bibliographic record
Abstract
Through the use of traditional environmental knowledge (TEK), the impacts of climate change on the Fort Albany First Nation community are explored. Thirty‐nine community members were interviewed using a semi‐directive interview format to gather knowledge about their observations of local environmental and climatic change and the significance of these changes. Thematic analysis, cluster analysis, and concept mapping were applied to analyze interview transcriptions. A second round of interviews was conducted to obtain feedback on the themes and concepts that emerged from the first round of interviews. Community members indicated that there have been noticeable changes in the timing of seasons, snow type, and total snowfall, with an increase in extreme weather events. These changes have impacted animal behaviour, traditional harvesting activities, and the winter road, which have led to socio‐economic and well‐being issues. The community has exhibited strength in adapting to ongoing changes in the environment; however, their ability to adapt to climate change in the future is not certain .
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Study of climate change impacts on a First Nation community; environmental and social effects, not research practice.
This studies climate-change impacts on a First Nation community, not the research system.
Community climate-impact ethnography of a First Nation; object is environment and well-being, not the research system.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".