Assembling Collaboration in the Debris Field: From Psychogeography to Choreographies of Assembly
Bibliographic record
Abstract
Between 2012 and 2017, Nova Scotia-based Narratives in Space + Time Society (NiS+TS) brought together more than 100 collaborators and hundreds of participants in several public art walks for Walking the Debris Field: Public Geographies of the Halifax Explosion. Through co-creation, including strategies of collaboration and sensorial storytelling in an embodied practice of creative citizenship, NiS+TS was able to generate—in time for the centenary of the devastating 1917 explosion in the Halifax harbour—a choreographed assembly that ably juxtaposed missing memories, histories, legacies, and present-day possibilities. Until the last few years, very few of the stories about the explosion and its aftermath included Mi’kmaq, working-class, immigrant, or African Nova Scotian experiences, even though these were communities deeply affected by the explosion. The first NiS+TS public art walk offering experiences of these stories drew fifty people, while the final attracted almost 200. The Debris Field events also resulted in the production of new forms of storytelling, including a free iOS app, ‘Drifts,’ and a shared website with the City of Halifax in time for the centenary ( intothedebrisfield.ca/ ). The kind of embedded, grounded power reflected in projects like Debris Field came through the growing assemblies of participants engendered by NiS+TS. The coordinated movements of such large bodies of people through spaces on which certain communities have been built, and from which other communities have been erased, makes more pertinent, and far less abstract, the deeply sedimented histories of class, gender, race, and mobility/ability in the 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".