Examining the Potential Use of Geospatial -Informatics Technologies to Engage Northern Canadian First Nation Youth in Environmental Initiatives
Bibliographic record
Abstract
Having experienced climatic warming before, First Nations people of the Albany River basin in sub-arctic, Canada, have already shown the ability to be adaptable to external influences. However, societal changes and the current accelerated rate of environmental change have reduced First Nations people ability to adapt. In addition, young people are no longer going out on the land as much. Fort Albany First Nation community members have commented on the lack of connection that some youth have with the land. A disconnect with the environment by youth can threaten the adaptive capacity of sub-arctic First Nations. As identified by Fort Albany First Nation community members, one potential tool that could influence the youth to become more aware of their land, is the collaborative geomatics tool. The collaborative geomatics tool is based on the WIDE (Web Informatics Development Environment) software toolkit. The toolkit was developed by The Computer Systems Group of the University of Waterloo to construct, design, deploy and maintain complex web-based systems. The collaborative geomatics tool supports a common reference map, based on high-resolution imagery. Three environmental outreach camps were held from 2011-2012, programming utilized place-based education as the platform to engage youth in their environment and community and begin using the associated mapping technology. All camps utilized the newly developed collaborative-geomatics tool and a camera ready handheld Global Positioning System (GPS) while participating in various activities that engaged them in their community and environment. The outreach program worked well in connecting youth with knowledgeable community members allowing for the direct transfer of traditional knowledge in a culturally appropriate manner, that is, learning through observation and doing, as well as other culturally-appropriate educational strategies. In addition, the informatics tool supported the archiving of this knowledge through the uploading of geospatially tagged pictures taken by the youth.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".