Tropical Futurisms: Making Futures
Bibliographic record
Abstract
Tropical Futurisms situates the making of futures in the geo-climatic zone of the tropics with its shared—yet always specific—histories of colonialism(s) and ecological biodiversities. At the same time, this special issue acknowledges the pluralities of tropical cultures and their cosmological insights, technological imaginings, and multispecies vitalities. This second part of the double Special Issue on Tropical Futurisms emphasises creative practices of future-making. It recognises the diverse ways of making futures by positioning them back in tropical material experiences in this time of escalating climate crisis. As with the previous issue on Thinking Futures, this second issue on Making Futures seeks solidarity in the tropics via imagining the future together in plural forms through creative practices. This issue offers insights from theatre performance, architecture, urban planning, street art, arts-nature exhibition, ethnography, photography, activism, film documentary, poetry, translation, and storytelling. It includes works from Tropical Africa, the Caribbean and Latin America, Tropical Australia, India, and the Southeast Asia countries of Thailand, Indonesia, the Philippines, Malaysia, and Sarawak on the island of Borneo. We are interested in the ways these creative works intersect across the pan-tropics, creating new rich and complex forms of future-making.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".