Bibliographic record
Abstract
I propose to read a selection of work from And Still We Laugh, a novel-in-short-stories told by Darryll 'Squito Bob, a halfbreed Nłeʔkepmx boy from a re-imagined small town in British Columbia's Fraser Canyon. The stories take place in the late-60s–early-70s, a tumultuous time in Canadian history. They draw from my experience as a mixed-blood Nłeʔkepmx, and member of the Lytton First Nation, as well as my experiences as a child and youth worker and foster parent, crisis line volunteer, and general observer of my world. Six of these stories have won or been shortlisted for prizes: "One Day at Recess," "Doug Bromley Must Die," "How Mosquito Got His Name," "T.H.E. King," "Salmon Song," and "Mavis Brown," which was also nominated for the Journey Prize and a Western Magazine Award. Six additional stories and a novella will round out the collection. Most of the stories in this project look at moments in other people's lives from 'Squito's perspective, exploring love, sex, child abuse, racism, rape and murder in a small, largely Nłeʔkepmx town. 'Squito has a unique view of his world, and his character shines through as he tells these stories. One of his strengths is his ability to make readers feel (somewhat) at ease, regardless of a scene's horror.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.186 | 0.171 |
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".