The City of Richgate: A/r/tographic Cartography as Public Pedagogy
Bibliographic record
Abstract
Abstract The City of Richgate project worked with eight intergenerational immigrant families and examined immigrant experiences and narratives through a community‐engaged process that employed a/r/tography as a methodology. As such, the research also investigated the extent to which a/r/tographical research could visually and narratively portray the analysis of data collected by the co‐a/r/tographers. After interviewing and collecting images from each family, large artistic gates (banners) were created. This first phase of the project revealed the power of images in situ, and thus the power of a/r/tography in situ. For the community members and co‐a/r/tographers meanings were constructed within ongoing a/r/tographic inquiries described as collective artistic and educational praxis. The second phase involved the identification of important places by each family within the City of Richmond. After analysing all of the data, several works of art were created with each family in mind: bus shelter images juxtaposing close‐up and far away geographical images; side‐by‐side images portraying historical and contemporary images of family ideals and/or issues; banners illustrating families in meaningful poses; and archival collections portraying the importance of identity and memory in the transformation of culture. This phase culminated in a citywide exhibition of the artwork performing public pedagogy. The exhibition questioned the idea of a City of Richmond having a community centre, and instead exhibited many Richgates, or conceptions of Richmond. Rather than having a city centre, there are many centres, a Network of Cities of Richgates, where centres are constantly changing and shifting to reflect the narratives of individuals living in a psycho‐geographical region of a city.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".