The Aboriginal Mapping Network : a case study in the democratization of mapping
Bibliographic record
Abstract
Land claims, increased control over natural resources and movement towards self-government demand that First Nations produce maps that bring local knowledge into planning and governance processes. For their mapping needs, First Nations are turning largely to geographic information systems (GIS), complex and expensive computer-based spatial database systems. They are, however, developing their technical skills independently of each other, rarely experiencing the knowledge-sharing benefits characteristic of an integrated community. To address this problem, and to help build mapping capacities in general, the Aboriginal Mapping Network was created by Ecotrust Canada, an environmental non-governmental organization, and several First Nations. Using the medium of the World Wide Web, the Network seeks to create linkages between First Nations mappers and to provide a space for the sharing of knowledge. This thesis uses a formative program evaluation framework to assess the strengths, weaknesses and potential of the nascent Network. The evaluation draws on interviews with First Nations mappers and network developers. Conclusions are drawn on how effective the Network is in developing communications linkages and facilitating knowledge sharing, and how this might continue in the future. Concurrently, the Network is used as a case study in the democratization of mapping. Capacity building in GIS technology, it is argued, will allow First Nations to produce unconventional maps that articulate local worldviews and perceptions of place. As embodiments of local knowledge, these maps will in turn be used in planning, negotiations and governance to empower First Nations on their own terms.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".