Bibliographic record
Abstract
The chapters and vignettes in this book are very much reflected in my and my wife’s life stories, and those of our parents, and grandparents. Our grandparents came to Canada at the beginning of the twentieth century. At the time, more than half the population of Canada lived in small towns and rural communities. Audrey’s grandparents ended up in the Okanagan Valley on her father’s side and in Alberta on her mother’s side. On her father’s side, the family faced the racism that was prevalent against anyone coming from Japan in the case of her grandparents and later, racism that all members of the family faced as Japanese Canadians. My grandparents ended up in Toronto and Hamilton. As Jewish immigrants from Germany, Poland, and Russia, they faced the antisemitism of the times even after they and their children (my parents and uncles and aunts) became Canadian citizens. In his final years, Audrey’s father moved back to the Okanagan Valley near where he grew up and where most of Audrey’s family still live. Audrey’s mother and stepfather aged in place in their home just outside of Kelowna. In recent years, it became apparent that they could no longer look after themselves and now live in assisted living. As their youngest son and after I left for university, my father and mother moved to a new house and lived there until he passed away. Soon after, my mother moved into a community for older people that had apartment buildings, communal dining rooms, various amenities, and a nursing home for those who could no longer live independently.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.223 | 0.089 |
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".