Towards improving cross-cultural dialogue and learning with maps
Bibliographic record
Abstract
This research focused on consulting with the O-Pipon-Na-Piwin Cree Nation (OPCN) people of Soutn Indian Lake, Manitoba to assess whether existing mapping practices could be improved to promote cross-cultural dialogue and leaming.In this regard, two existing maps of the OPCN registered trapline area were reviewed to probe perceptions of the maps' adequacy in representing Cree space and for uncovering lessons to represent that space in a more culturally appropriate way.Research results indicated that there are many def,rciencies inherent in the typical cartographic representation of Cree space, particularly in the realms of map content, construction and the message that maps purvey.Seventeen map elements evolved out of research findings that, when implemented in mapping, would improve the conditions for cross-cultural dialogue and learning.These map elements were then compared to existing mapping practice in environmental assessment (EA) from the Wuskwatim Clean Environment hearings to uncover any methodological deficits in EA mapping.Findings indicated there was notable room for improvement in this regard.Supplementary study is recommended to fuither refine these research findings to improve mapping practice in EA.We are entering what the early explorers described on ancient maps as "terÍaincognita," an unknown land...While these were unknown lands for the early explorers, this was not true for the original people who served as guides for the newcomers.... Perhaps in our search for technical solutions, we have lost sight of the spirit needed to guide us in our search, and we need to turn to our ancient guides once again.
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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 | 0.055 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".