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Tropical Futurisms: Making Futures

2025· article· en· W4409652020 on OpenAlexfundno aff
Ysabel Muñoz-Martínez, Jingyao Hu, Kenneth Toah Nsah, Anita Lundberg

Bibliographic record

VenueeTropic electronic journal of studies in the tropics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
FundersAgence Universitaire de la FrancophonieRadboud UniversiteitUniversité de LilleUniversität zu KölnAarhus Universitet
KeywordsFutures contractAstrobiologyArtHistoryLiteratureEconomicsFinancial economicsPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.327
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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