Unravelling the seams: text(ile)s of compassionate un/settling
Bibliographic record
Abstract
In this performative inquiry, I stitch my own experiences of un/settling in an educational context together with an art-ful practice of contemplative quiltmaking. This study is in response to the Truth and Reconciliation Commission of Canada’s Calls to Action for education. Through performative inquiry, I take moments that call my attention—un/settling moments—to the fabric and ask, “What do I need to know, right here, right now?” What happens when a teacher/a community member/a family member unmakes/unsettles/unlearns with a quilt in her hands? What does she learn about unsettling? Throughout the inquiry, care is taken to centre compassionate landscapes of knowing, being, and becoming, in the making of quilts, research inquiry, and texts that communicate knowledge and knowing. A significant aspect of the study is its context; a small, rural, farming, town in the Canadian prairies settled in the early 1870s. I draw on autoethnography to understand un/settling moments in relation to culture, society, and family. Readers are invited to read a thesis, unconventional in form and style; an embodied step towards un/settling.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.042 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".