Bibliographic record
Abstract
In this essay, I examine opportunities for re-viewing what I call, after John Law, examples of “one-world world” arguments through the lens of negotiating pluriversality. Specifically, I look at examples of arguments that placed side-by-side suggest that life will not return to what life was like in the recent past due to accumulating social crises and ecological disasters that have been increasing in both amount and intensity. What links these arguments is a one-world world presumption that we are all in this together. My approach is to disentangle and re-view the arguments separately as accounts of experiences of conjugated power relations and gray space. I propose that re-viewing one-world world arguments for purposes of negotiating pluriversality benefits from, following Achille Mbembe, “the analyst [revealing] the myriad ways ordinary people guide, deceive, and toy with power instead of confronting it directly” and, following Ariella Azoulay, remaking a “never-ending series of [constitutive] moments … into a never-ending project.” Ultimately, I argue negotiating pluriversality involves ascertaining ways communities in transformation can, as Azoulay puts it, reinvent the ways they “tie themselves together.”
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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.080 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".