Bibliographic record
Abstract
I could never have imagined, when I began this project as a graduate student at the University of Victoria, how large it would ultimately become; how long it would take to finish, and how much support I would need along the way.I began this project on unceded Coast Salish territory.I finished it on traditional Huron-Wendat, Anishnaabe, and Mississauga lands.I have used more resources on these territories than any uninvited guest has a right to expect.I'm grateful for the gift of treaty citizenship.May the future, and our relations within it, be more just.I've been honoured to have been granted access to the personal documents, family histories, and work of so many writers.To all the people who so generously entrusted me with their writing, I am grateful.I hope this book does your writing, your families, and your histories justice.I am grateful to the women-the matriarchs and knowledge-keepers of the prairies-Velma Carter, Wanda Leffler Akili, Gwen Hooks, Cheryl Foggo, Junetta Jamerson, and Crystal Mayes, among others, who have kept memory of the prairies' black histories, stories, and food
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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.338 | 0.233 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".