Examining the Potential Use of the Collaborative-Geomatics Informatics Tool to Foster Intergenerational Transfer of Knowledge in a Remote First Nation Community
Bibliographic record
Abstract
Northern First Nations in Canada have experienced environmental change throughout history, adapting to these changes based on personal experience interacting with their environment. Community members of Fort Albany First Nation of northern Ontario, Canada, have voiced their concern that their youths’ connection to the land is diminishing, making this generation more vulnerable to environmental change. Community members previously identified the collaborative-geomatics informatics tool as potentially useful for fostering intergenerational knowledge transfer. In this article, we assess the potential of the informatics tool to reconnect youth with the surrounding land in order to strengthen the adaptive capacity of Fort Albany First Nation. The tool was introduced to students in an environmental-outreach camp that included traditional activities. Students used global positioning systems and geo-tagged photographs that were loaded onto the informatics tool. Semi-directed interviews revealed that the students enjoyed the visual and spatial capabilities of the system, and recognised its potential to be used in conjunction with traditional activities. This pilot study suggests that the tool has the potential to be used by youth to provide an opportunity for the intergenerational transfer of Indigenous knowledge, but further evaluation is required.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".