Bibliographic record
Abstract
My father was an excellent teacher who rose to become a school superintendent in Hong Kong's Department of Education.Speaking fluent English and with a teaching certificate recognized in Canada, he assumed he would have no trouble finding a job when he, my mother, and I emigrated and moved to Toronto in 1970.Well, no.There were too many out-of-work teachers at the time, and the job market was poor.So he did some supply teaching, and when he couldn't make a living, he drove taxi or delivered Chinese food.After two years of struggling to find meaningful work, he suffered a nervous breakdown.And he became abusive towards my mother.For her part, my mother, too, had been a qualified teacher in Hong Kong.But she couldn't land a job here in her field either.Because she needed a job to help make ends meet, she had to forego her study of English as a second language and never did get the chance to learn English properly.So, my mother worked in a hotel, first as a maid and then as a helper in the basement laundry department, where she hauled heavy, wet sheets and linens all day long.Years of hard work.Unfulfilled dreams.Pain in her hands from manual labour.The sum of it all made her a bitter and negative person, as did the beatings my mother endured as a result of my father's fragile mental health.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.576 | 0.533 |
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".