Reclaiming Geospatial Data and GIS Design for Indigenous-led Telecommunications Policy Advocacy: A Process Discussion of Mapping Broadband Availability in Remote and Northern Regions of Canada
Bibliographic record
Abstract
Abstract Geographic Information Systems (GIS) and geospatial data are important advocacy tools adopted by a range of users, including telecommunications policy advocates. However, without the means to actively deconstruct and reshape such platforms, reclaim the geospatial data they utilize, and generate the visualizations they produce, the increasing adoption of these resources threatens to disempower some community-based user groups. In this article, we argue that the processes used to design such tools for policy advocacy must transparently reflect the socially constructed nature of the GIS systems and the geospatial data visualizations they generate, as well as the values and goals of the specific user groups they are designed to support. We ground this argument in a case study of a regulatory hearing on telecommunications infrastructure and services in Canada, and introduce a freely available online resource that documents our GIS design workflow in more detail.
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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 | 0.053 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.037 | 0.038 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".