Bibliographic record
Abstract
This article revisits the three foundational principles of Participatory Mapping practice identified in Good practices in participatory mapping. These include processes that strive for transparency, are unencumbered by time, and prioritize trust - the ‘Three T’s’. Authors Kelly Panchyshyn and Jon Corbett analyze the relevance of these principles under the spectre of the global COVID-19 pandemic. This reflection is carried out within the context of Kelly’s Master’s research. Over the course of 2020, Kelly worked with staff and citizens of the Kwanlin Dün First Nation to map Indigenous and non-Indigenous plant harvest foodways within Łu Zil Män, an expansive stretch of land on the edge of Whitehorse, Yukon. In exploring both the barriers and opportunities created by conducting this project during a pandemic, the authors determine that the ‘Three T’s’ remain essential for conducting meaningful participatory mapping. However, they also argue that each T takes on new dimensions within contexts of isolation and social distancing, particularly for Northern and Indigenous communities.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.249 | 0.055 |
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".