Bibliographic record
Abstract
In this article, Doonan analyzes two performances presented by the SensoriuM, a collaborative participatory art platform in Montreal, Quebec. In doing so, she shows how the SensoriuM makes publicthrough curatorial and dramaturgical practice. By making public,she refers to the active translation of materiality into representational forms, and also to the assembling of humans and non-humans in participatory performance. Doonan describes the concrete audiences bounded by the live events, as well as the more amorphous and immeasurable publics that are brought into being through the circulation of texts, including digital images, videos, and oral presentations. Drawing from science and technology studies and more-than-human geographies, she explores “epistemic publics,” or the animal, organic and machinic configurations that come into being in the process of creating (objects of) knowledge. Doonan analyzes two specific performances: Midsummer Mile End Foraging Tour and Hunter, Gatherer, Purveyorto show how these processes of making public are enacted. Both performances use food as a medium to complicate and undo binaries of public/private, self/other, domestic/wild, depressed/revitalized. The meanings and uses of particular places are brought into question through embodied and symbolic means. Doonan argues that these performances work to de-design the city by queering its dominant discourses and creating intimate spaces of exchange.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".