Introduction: Making Worlds in the Pluriverse
Bibliographic record
Abstract
In 1996, the Zapatista uprising in Chiapas (Mexico), formed by peoples of Mayan descent, declared that in “the world we want, everybody fits. The world we want is a world in which many worlds fit.” Since then, their vision of “a world where many worlds fit” has given rise to the concepts of the pluriverse and pluriversality. The concept of the pluriverse has gained interest worldwide with texts such as Design for the Pluriverse, Constructing the Pluriverse, A World of Many Worlds, and Pluriverse: A Post-Development Dictionary, bringing broader attention to the work done by many activists, peasant, and Indigenous groups in Latin America and beyond. It is no surprise that the concept of the pluriverse has been embraced in non-Western parts of the world. In turn, our special issue asks: What is the pluriverse in North America, arguably one of the centres of Western, Eurocentric power? The artistic and scholarly contributions that make up this special issue investigate the crossroads, the encounters, the divergences and convergences that build new possible worlds, and the friction of incommensurabilities, in a North American context, and illustrate the potential worlds that we, as humans, non-humans, and more-than-humans alike, could share.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".