Bibliographic record
Abstract
Th is book has been a labor of love.It was born across two continents and three different cities, and from somewhere in the constant back-and-forth between everyday spaces of family and work.Long Skype conversations, even with intermittent dropped lines, and exchanges during sporadic crossed paths at conferences allowed us to brainstorm and plan its beginnings.Th e seeds of early thoughts on anthropology and Catholicism were sown in a 2009 Latin American Studies Association meeting panel in Rio de Janeiro, then inspired by the very generous comments of our panel discussants, Manuel A. Vásquez and José Casanova.A key moment spent together in a lovely fi ve-day hiatus in March 2014 in Valentina's warm Toronto attic, fueled by wine, pasta, and long walks, allowed us to consolidate critical ideas and the basic structure.But it was the belief all three of us had in the need for this book that kept us going over the long months.Somehow once we got started the book seemed to take on a life and momentum of its own.Far more than an intellectual project, it has been about experiential cross-continental learning.We cannot mention all the people who have been enabling, in diff erent corners of the world, the thinking for and the making of this book.But we would like to thank, above all, our editor Reed Malcolm for having faith in this project right from the start and for shepherding us so expertly through every turn and glitch.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.315 | 0.210 |
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".