Bibliographic record
Abstract
This dissertation shares what was learned through collaborative Indigenous research in Toronto about the meaning of grief medicines: the sources, practices, and relations of care, made and sustained by communities through ongoing experiences of loss. Building on Indigenous storywork (Archibald, 2008) as a methodology, this research learned from 13 Indigenous grief workers as well as through the land (Simpson, 2014), by caring for plant medicines in gardens. The theoretical and methodological approach to this project engaged Anishinaabeyendamowin (Anishinaabe thought), Indigenous feminist, two spirit, queer, and trans approaches to research, and theorizing towards what it means to reduce the harms of settler colonialism, and intersecting systems of violence, in communities. This dissertation discusses what was learned through this research about grief, medicines, and healing; as well as the care, collaboration with older-than-human worlds, and ceremonies which can support life. This project is an offering to community members seeking to better understand life, death, and grief: what it is to feel and experience, here in the physical world, the transition and transformation of physical life to spirit, again.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".