Enhancing the Interpretability of Multicriteria-Spatial Decision Support Systems (MC-SDSS) for non-SDSS-users: a Case Study of Sahtú-led Boreal Caribou Range Planning in Northwest Territories
Bibliographic record
Abstract
This research explores the intersection of Spatial Decision Support Systems (SDSS), Indigenous knowledge, and community-led planning in the context of boreal caribou (Tǫdzı) range planning in the Sahtú Region. The goal was to facilitate collaboration between government planners and Indigenous governors and communities by developing the design of an interactive, map-based web application—the Tǫdzı Atlas. As part of a multi-partner research project, we employed a multi-phased approach, including interviews and workshops with Dene community members in Tulíta. I identified crucial factors and regions for boreal caribou planning, and functionalities and information display methods for a digital atlas, driven by community input. Addressing communication challenges in SDSS for non-SDSS-users, I proposed methods to enhance interpretability and usability including using dynamic visual aids, interactive functions, and real-time changes. This research provides concrete guidelines for the future development of the Tǫdzı Atlas, fostering connections between Indigenous knowledge and SDSS and promoting mutual understanding.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| 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".