Bibliographic record
Abstract
This work is a conversation with my fears and wonder in the early days of the COVID pandemic, in which I had to live and work as a high school teacher, parent, scholar and artist. Through the weaving of my poems, paintings and life writing in an Indigenous Métissage, I reached for the teachings I have learned from my Elders and Ancestors, longing to find ways to stay human during those most inhumane days, when dis-ease was worsened by social injustice. Through the refracted world of the pandemic came the distressing news of recoveries of unmarked graves at the sites of former residential schools for Indigenous children. This news re-wrote me, unwound me, and re-routed the direction I longed to go to make learning spaces better for youth. Through poetic inquiry, I attempted to process these findings and asked Creator and All My Relations, how to be useful to the work of healing in this dis-ease.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.055 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".