Claiming individuality : the cultural politics of distinction
Bibliographic record
Abstract
Acknowledgements 1. On Claiming Individuality: An Introduction to the Issues Vered Amit (Concordia University in Montreal) and Noel Dyck (Simon Fraser University in British Columbia) 2. In the Aftermath of Death: Presenting Self, Individuality and Family In An Iyangar Family in Chennai, 1994 - Mattison Mines (University of California, Santa Barbara) 3. Growing up in the Caribbean: Individuality in the Making - Karen Fog Olwig (University of Copenhagen) 4. Distinction and Co-Construction: Diaspora Asian Fashion Entrepreneurs in London - Parminder Bhachu (Clark University in Massachusetts) 5. Claiming Individuality through 'Flexibility': Career Choices and Constraints among Traveling Consultants - Vered Amit 6. A Personalized Journey: Tourism and Individuality - Julia Harrison (Trent University) 7. Becoming Educated, Becoming an Individual? Tropes of Distinction and Modesty in French Narratives of Rurality - Deborah Reed-Danahay (University of Texas at Arlington) 8. An Anatomy of Humour: The Charismatic Repartee of Trevor Jeffries in the Mitre Pub - Nigel Rapport (Concordia University, Montreal) 9. Proclaiming Individual Piety: Pilgrims and Religious Renewal in Cote d'Ivoire - Marie Nathalie LeBlanc (Concordia University) 10. Claiming to be Croat: The Risks of Return to the Homeland - Daphne Winland (York University) Notes on Contributors Index
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.085 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".