Bibliographic record
Abstract
In “Imaginary Spaces of Conciliation and Reconciliation,” Métis scholar and artist David Garneau reflects on two paintings that document gatherings through comic-style images with speech bubbles, but no text. These works, as Garneau explains, serve as mnemonic devices that capture the relationships, exchanges, and emotions in the room without revealing the content, relying on a coded visual language that requires deeper understanding (Garneau, 2012). This act of omission can also be seen as a form of refusal, acknowledging that some things are not meant to be shared (Simpson, 2007). Inspired by Garneau’s approach, I translated my reflections from the 2024 Mawachihitotaak conference into autoethnographic field notes through beadwork on home-tanned fish skin leather. Each design, color, and symbol carries personal meaning, allowing me to decide what to reveal and what to conceal. While those present at the conference may interpret the beadwork with deeper insight, outsiders have a more limited understanding. Drawing from Michelle Porter’s exploration of memory mapping and oral storytelling (Porter, 2024), I pair beadwork photographs with select conference quotes, offering potential context without translating the full narrative. This work enacts refusal by not sharing every embedded story, as they are not mine to fully tell.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".