Bibliographic record
Abstract
To say that I was overwhelmed with the invitation to speak this afternoon would be an understatement.Thank you for having me.It has been a very long journey for me to reach this podium.I often attend conferences, workshops, and events such as this aimed at front line service providers, academics, and others who are passionate about eliminating violence against women.However, quite often the voices of the women who have survived violence are missing.I hope that in the few minutes allocated to me, I will be able to give you a glimpse of what it is like to live in an honour-based culture; where a female knows from the day she is born that her life is in danger because she was born the wrong gender.2 I was born in India, the oldest of seven children-six girls and one boy.My formative years were governed by three constants: my father's service in the Seventh-Day Adventist Church; the culture of honour and shame that dictated the behaviour of my family and everyone I knew; and my yearning for an education that continually eluded me.When I was seventeen my parents arranged for me to be married to a much older man, and at twenty-one I immigrated to Canada with two young daughters, an abusive husband, and the equivalent of third grade education.There I slowly awoke to the rights and protections Canada offered women.After embarking on this long and frightening journey, I ultimately achieved two master's degrees, founded three agencies, which assist women abuse victims, and wrote my memories in Unworthy Creature: A Punjabi Daughter's Memoir ofHonour, Shame and Love.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".