Book Review of Zafar, Samra (with Meg Masters). (2019). A Good Wife: Escaping the Life I Never Chose. Toronto: HarperCollins Publishers Ltd.
Bibliographic record
Abstract
A Good Wife: Escaping the Life I Never Chose", is a memoir through which she tells her heartbreaking yet inspiring story in detail of how her life changed significantly from living a stress-free and independent childhood in Abu Dhabi with her family, to living in an abusive marriage in Canada as a young adult and to eventually gaining back her freedom.As a teenager, Zafar had many dreams she wished to accomplish to instill a sense of meaning and a purpose in her life; one of them was to acquire a post-secondary education but this dream was initially taken away when she instantly married a man eleven years her senior at the age of seventeen.Zafar's husband, Ahmed Khan and his family promised her countless times that the marriage and move to Canada would be a fulfillment of her dreams but as time went by, after facing both physical and emotional abuse while living in a prisoned marriage filled with constant fear and being labelled as her husband's property, Zafar soon realized that all those promises were nothing but empty, broken words.Soon after the birth of her two daughters, she was now more than ever determined to build a new future for herself.She eventually got the opportunity to attend the University of Toronto and as of today, she is a mentor and public speaker who uses her story to inspire others.Throughout her book, Zafar illustrates the importance of fighting for a life of
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.082 |
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".