“It's not being ‘on‐the‐land,’ it's like we are a part of the Land”: Indigenous youth share visual stories at “on‐the‐land” camps in the Dehcho
Bibliographic record
Abstract
Abstract In northern Canada, Indigenous Peoples face rapid environmental and sociocultural changes that disrupt access to traditional places and practices. This disruption limits cross‐generational knowledge transfer and decreases opportunities for youth to connect with the Land. Land‐based education programs aim to provide Indigenous youth with spaces to learn about and connect with the Land, their language, and culture. However, there is limited research on what youth experience, enjoy, and learn during these programs. Our paper explores this through a study with Dene and Métis youth in the Dehcho region of the Northwest Territories. We investigate what youth experience and learn during “on‐the‐land” camps and how these camps provide spaces for youth to connect with the Land. Using a community‐based, Indigenous research methodology, we employed photovoice, allowing youth to share visual stories of their camp experiences and their (re)connection with the Land. Our results are categorized into three themes: (1) connections with the Land; (2) enjoyment of the camp environment; and (3) land‐based learning. We argue that land‐based education programs, such as on‐the‐land camps, are effective for fostering relationships between Indigenous youth and the Land. These camps teach youth various ways to connect with and protect the Land for future generations .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".