Cutting Away from Smooth Space Alfred North Whitehead's Extensive Continuum in Parametric Software
Bibliographic record
Abstract
Once largely ignored, the speculative philosophy of Alfred North has assumed a new prominence in contemporary theory across the humanities and social sciences. Philosophers and artists, literary critics and social theorists, anthropologists and computer scientists have all embraced Whitehead's thought, extending it through inquiries into the nature of life, the problem of consciousness, and the ontology of objects, as well as into experiments in education and digital media. The Lure of Whitehead offers readers not only a comprehensive introduction to Whitehead's philosophy but also a demonstration of how his work advances our emerging understanding of life in the posthuman epoch. Contributors: Jeffrey A. Bell, Southeastern Louisiana U; Nathan Brown, U of California, Davis; Peter Canning; Didier Debaise, Free U of Brussels; Roland Faber, Claremont Lincoln U; Michael Halewood, U of Essex; Graham Harman, American U in Cairo; Bruno Latour, Sciences Po Paris; Erin Manning, Concordia U, Montreal; Steven Meyer, Washington U; Luciana Parisi, U of London; Keith Robinson, U of Arkansas at Little Rock; Isabelle Stengers, Free U of Brussels; James Williams, U of Dundee.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".