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Record W3081096184 · doi:10.1080/17445647.2020.1805806

A community farm maps back! Disputes over public urban farmland in Calgary, Alberta

2020· article· en· W3081096184 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Maps · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of Calgary
FundersMitacs
KeywordsVernacularVolunteered geographic informationSituatedGeographyState (computer science)PoliticsDigital mappingSociologyCartographyPolitical scienceComputer scienceLawLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.278
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it