Bibliographic record
Abstract
thIs book Is the culmInatIon of many years of work on Griffintown.I first came to Griffintown over a decade and a half ago, curious about the history of what was then a derelict and empty neighbourhood.Griffintown has been good to me.I have met and collaborated with a host of former residents of the neighbourhood, been involved in the grassroots fight against gentrification for the sake of gentrification, helped to ensure the survival of the Griffintown Horse Palace, got my name in the newspaper, saw my face on TV, heard my voice on the radio, and made my Mom proud.I have also worked with Montreal artist and filmmaker G. Scott MacLeod for the past few years to develop a website of twentyone short films about Griff (http://griffintowntour.com).My grandfather, Rod Browne, introduced me to Griff twenty-five years ago.On a grey day in March 1992, he took me on a tour of the Montreal he grew up in.He taught me about where I come from, where our people were from.All I knew then was that we were from Montreal, but he taught me about the Irish, about NDG, and about the Irish in Verdun, Pointe-Saint-Charles, and Griffintown.My grandfather and my grandmother, Eleanor Shipman Browne, were my biggest fans, and I lost something when they died.I like to think that that day in March 1992 is the reason I became a historian.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.372 | 0.257 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".