A community farm maps back! Disputes over public urban farmland in Calgary, Alberta
Bibliographic record
Abstract
Geographers, cartographers, and related social scientists are increasingly locating the (geo)politics of the vernacular within volunteered geographic information, the geoweb, and other digital technologies that enable the production of new maps. We instead focus our attention on ‘old’ cartographic practices. We contend that map-based community activism and geopolitics continue to occur in ways that much research has left behind in its shifted attention toward digital geographies. We conceptualize vernacular counter-mapping, as practiced by Grow Calgary a community urban farm located on public land, by focusing on vernacular cartographic method and mode. We argue first that the vernacular exists not just in the production of new maps but also in the practice of altering and re-narrating existing maps, and, second, that the vernacular exists not just in the new modes of VGI and distributed/crowdsourced data production, but in the mode of leveraging official, static state maps to make legible situated knowledges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".