Bibliographic record
Abstract
do you come from? When Vijay Agnew first immigrated to Canada people would often ask her do you come from? She thought it a simple, straightforward question, and would answer in the same simple, straightforward manner, by telling them where she had been born and where she grew up. But over the years she learned that many so-called people resent being asked this question, because it implies that having a different skin colour (which is what usually prompts the question) makes a person an outsider and not really Canadian. This realization inspired her to look more closely at the question -- and the answer. The result is this book. Where I Come From is a reflective memoir of an immigrant professor's life in a Canadian university. It covers the period from 1967, when Canada was opened up to immigrants, to the present. The book illustrates the ways in which identity is socially constructed by tracing some of the labels that were applied to the author at various stages during her thirty years in Canada -- foreign student, Indian woman, immigrant, Indian and third-world woman. She shows how each of these names has affected her relationships with other people and contributed to making her the woman she is now perceived to be: a feminist, anti-racist, activist professor. This multilayered story reveals the complex ways in which race, class, and gender intersect in an immigrant woman's life, and engages readers in a conversation that narrows the distance between them, showing not only what is different, but what is shared.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.169 | 0.118 |
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".