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Record W4399799988 · doi:10.21606/drs.2024.169

Pluriversal Design as a Paradigm

2024· article· en· W4399799988 on OpenAlexaff
Renata Leitao, Lesley-Ann Nöel, Maria Rogal, Nicholas B. Torretta, Juan Montalvan, Sucharita Beniwal

Bibliographic record

VenueProceedings of DRS · 2024
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The concept of the Pluriverse refers to a world where many worlds fit. But what is pluriversal design? While it has been used as a synonym for initiatives around diversity, equity, and inclusion, this track argues that pluriversal frameworks represent a distinct paradigm — in contrast with the universal design paradigm. These two paradigms, while important in their own right, deal with diversity and plurality in fundamentally different ways. The term ‘universal’ is grounded in the belief that we all live in one single world, with one right (or “developed”) way to live, with a dominant narrative in which the main characters have been affluent white men from the Global North. The universal paradigm is about convergence, normalization – and sometimes assimilation, othering, exotification, or tokenism. Within this paradigm, designers strive to cater to multiple cultures and diverse users, reduce deficits, increase access, and include marginalized perspectives – e.g., making people of color play significant roles in the dominant world narrative without transforming the underlying plot. The term ‘pluriversal’ recognizes there are many possible ways of being and world-making — multiple worlds and alternative narratives exist, and people from diverse cultures and geographies are struggling to enable alternative plots to flourish. Therefore, a pluriversal design paradigm is grounded in divergence. Pluriversal designers focus on, for instance, societal transformation, self-determination of local communities, alternative ways of world-building, and the interdependence of all beings. This track welcomes papers that explore this conversation/argument or how pluriversal frameworks can be manifested/nourished/encouraged in design practice.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.033
Scholarly communication0.0220.025
Open science0.0030.013
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0150.004

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations3
Published2024
Admission routes1
Has abstractyes

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