Bibliographic record
Abstract
Use of the term “intersections” in the title of this book undoubtedly evokes impressions of postmodernist rhetoric, yet it is also a term long used by symbolic interactionist sociologists to analyze “intersecting” lines of action and social worlds. This interplay of postmodernity and tradition is precisely one of the “intersections” we undertake to explore in this volume. Further intersections arise between the various analytical approaches exemplified by the authors represented in this book, and also between the human actors, the tools, the entities and the bodies that are constitutive of the new medical technologies. How these intersections relate to each other, in other words, how new biomedical objects and subjects call for new kinds of analyses, is one of the issues raised by the present collection of articles. As indicated by the book's title, one can work with the new medical technologies, and we all live, directly or indirectly, with them. Some of the contributors tend to focus on the “working” side of this equation, others on its “living” side, while all struggle, more or less openly, to bring these two sides together. The authors display many differences in their choice of topics and approach (two not entirely independent elements). However, they share an understanding of “body politics” that, instead of rejecting or accepting recurring dichotomies such as that between Nature and Culture, looks at how dichotomies are produced. Yet, rather than focusing on either differences or commonalities, it seems more interesting to us to look at intersections, that is, temporary convergences that can lead to advances on some particular problem, with no pretence of providing a comprehensive world-view or a theoretical manifesto.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.475 | 0.283 |
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".