Bibliographic record
Abstract
Tis collection is the result of fruitful conversations over a number of years among the editors and contributors.It brings together the work of scholars in diferent disciplines and with diverging interests, many of whom had never met one another -and some of whom we have yet to meet.Several authors tested out their ideas by presenting early versions of their chapters at meetings of the Canadian Historical Association in 2016 and 2017, and many of us came together for a collection workshop, held at the University of Toronto in 2017.In developing the open call for the collection, we asked potential contributors to think about their respective research topics in diverse areas of second-wave feminism, a period broadly construed from the perspective of the history of emotions.What, we asked, could insights from the history of emotions and afect theory bring to the history of Canadian second-wave feminism?Te collaborative and engaged process of speaking together and learning from each other has strengthened this collection and allowed us all to think in imaginative and interdisciplinary ways about feminist history.Te results are an intriguing and diverse set of chapters that challenge the timelines and chronological markers of feminist activism, explore the intimacy, joy, power, and despair of social activism, and ultimately ask us to take seriously the extraordinary political power of collective action, emotion, and feeling.We thank the contributors for undertaking this intellectual and emotional journey with us.We also extend our gratitude to the anonymous peer reviewers for their detailed and thoughtful suggestions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.136 |
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".