Bibliographic record
Abstract
Story collection—the practice of generating art or research by gathering participant narratives and combining them into a single product—is a popular method used by socially engaged artists and art education researchers. This ethnographic case study germinated with the examination of a story collection artwork executed at a gallery in a co-governed Indigenous–settler community in northern Canada. It expanded to consider how both my ethnographic methods and social practice art enact extractive, settler colonial approaches to knowledge. Working from Indigenous studies scholarship, particularly Sean Glen Coulthard (Yellowknife Dene), I show how repeated misrecognition of what Indigenous (and even settler) residents valued contributed to a local phenomenon of storytelling fatigue. This is a form of exhaustion brought on by excessive requests from outsiders to contribute to a project or artwork. These findings signal the need for artists, galleries, educators, and researchers to scrutinize how their undertakings are informed by the settler colonial politics of recognition.
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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.044 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".