Bibliographic record
Abstract
I grew up in a city as segregated as colonial Calcutta.On weekends when my street was dead, I invented a game for myself.I would get on a bus heading east out of my neighbourhood, and then keep transferring, jumping off every time a new bus drew up into my path.My travels took me to the rows of redbrick workers' housing in the east end, to the industrial boulevards of the north, and to my favourite destination, the crumbling and fetid warehouses along the river.I was barely a teenager and I kept these trips a secret.They were a first venture into transgression, and powerful lessons in cultural difference.Any child growing up in a big city can visit countless villages for the price of a bus ticket.Many of those villages will be strange, perhaps unwelcoming.But when cities are divided by language, there is no doubt.The voyage will take you to foreign territory.The Montreal I knew as a child thrived on difference.When my mother spoke of "our Easter" or "our Christmas" to describe Jewish holidays, the nods of tradespeople were understanding.Everyone belonged to some clan and was expected to observe the rules of membership.My visits to churches, the afternoons spent on barstools in east-end restaurants -these were spurred by curiosity.But in the tidy, sectarian city, they were acts of disorder and intrusion.One weekend, when I was still in high school, I got lucky.A model United Nations Assembly was organized for students across the city, and I was put up for the weekend in a convent.Finally, after wandering around its margins, I was right in the belly of difference.As I walked preface
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.362 |
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".