Bibliographic record
Abstract
In so many ways this is a book about love.It has, in all aspects of its creation, been a labour of love, and as such owes deep debts of gratitude.We will no doubt forget to mention someone who supported us along the way, so we start by just saying thank you to everyone who saw something in this project and enabled it to come to fruition.We would like to thank the Social Sciences and Humanities Research Council of Canada (SSHRC) for a workshop grant for "Critical Reflections on Polygamy" which enabled us to meet face to face in Victoria, British Columbia, from 1 to 3 October 2009.That gathering allowed all of us who travelled to Victoria to engage with each other and the difficult questions generated by the topic of legal and illegal family forms, and to develop and workshop ideas into the forms published here.Thank you also to SSHRC for the grant through the Aid to Scholarly Publications Program, an initiative vital to academic publishing in Canada.Thank you to everyone at the University of Victoria, Faculty of Law for the hospitable and welcoming environment for the workshop, with special thanks to Rosemary Garton, Sandra Leland
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.190 | 0.102 |
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".