Bibliographic record
Abstract
This book began with my being unable to answer students' questions about the absence of women in the planning history they were learning.I was working, after all, in the field of planning ethics and, while I had begun to think about feminism, feminist ethics, and planning, I did not imagine myself to be a historian.Canadian history was a required course when I attended Quebec High School, and I did not do well.First, I have a bad memory, and history courses entailed memorizing dates, names, and facts that were generally long ago and far away.Second, I was known by my teachers for not applying myself in areas that did not interest me.Put those two things together and I found myself worrying about passing the course and being able to graduate.I squeaked through with a grade well below those I received in biology, English, and (even) geography (in French).Many years later, it was the planning students' persistent queries and curiosity about women in Canadian planning history that encouraged me to overcome these obstacles and begin work in a subfield of planning that I had successfully avoided for a long time.My thanks go to the students I have taught in the School of Urban and Regional Planning at Queen's University; without them, I would not have embarked on this path.More particularly, several students contributed as research assistants to my work.Emma Fletcher, Terence Leung, Richard McCabe, Peter Walberg, and George Clayton helped with reviewing literature, locating participants, and getting the project off the proverbial ground.Paul Sajan did a lot of initial archival work and, with his constant good cheer and commitment,
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.380 | 0.238 |
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".