Bibliographic record
Abstract
Artists \nAymara Alvarado Lang (Gatineau), Arquitectura Expandida (Bogotá), Center for Urban Pedagogy (New York), Abigail Child (New York), Tony Cokes (New York), Guerilla Girls (New York), Joar Nango and Tanya Busse (Tromsø), Kika Thorne and Adrian Blackwell (Toronto) / October Group and February Group, Michael Rakowitz (Chicago), Neal Rockwell (Montréal), Frank Shebageget (Ottawa) and Andrea Luka Zimmerman (London) \n \nCurators \nJason St-Laurent (Ottawa) and Serkan Taycan (Istanbul) \nAdvisor: Stefan St-Laurent (Gatineau) \n \nThe exhibition Give Me Shelter presents 12 artistic responses to the issue of housing insecurity and homelessness over the last few decades, featuring work by Canadian and international artists and architects. Focused on social practice and community activism, the participating artists propose a wide range of projects that shed light on this ever-pressing issue. Together, the works in the exhibition urge us to consider how we might create more equitable and compassionate systems that provide safe housing for all. \n \nGive Me Shelter is the inaugural edition of SAW’s new triennial exhibition of art and activism.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.582 | 0.337 |
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".