Cultivating Change : using the GeoWeb to map the Local Food System in the North Okanagan of British Columbia
Bibliographic record
Abstract
Grassroots advocacy organizations seek novel ways to bring their message to the masses. The Geospatial Web (GeoWeb) is changing the way maps can contribute to communication strategies for advocacy efforts. In the North Okanagan of British Columbia the local food movement is a case for advocacy. I employ an Action Research process to the evaluate ways that organizations advocating for building a local food movement use the GeoWeb for social change efforts: I examine how a community organization, Food Action Society of the North Okanagan negotiates and utilizes the GeoWeb to address localized food security concerns and to strengthen the local food movement. To evaluate the GeoWeb as an advocacy tool, a Web Portal called Okanagan Food Portal was developed as a platform to host diverse information such as maps, directories and videos about local food in the region. Methods to evaluate the project include participatory observation, focus groups, questionnaire and semi-structured interviews. Results examine three areas, the politics of hyper-local media, perspectives of local food advocates and the feedback from the public demonstrations. The results reveal that the while GeoWeb offers new opportunities for counter-mapping and Public Participatory Geographic Information System (GIS) approaches, many advocates in smaller communities still cannot effectively utilize the mapping tools. The limited ability of smaller volunteer organizations to independently access these technologies reduces the ability for effective participation on the GeoWeb and therefore its applicability for advocacy in the community. In addressing the question of how the GeoWeb influences social change efforts in the North Okanagan local food movement, this thesis seeks to contribute to the wider discussion regarding how the GeoWeb may address longstanding issues of unequal power and access in mapmaking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".