Pluriversal design and desire-based design: desire as the impulse for human flourishing
Bibliographic record
Abstract
A world of many centers is a world where multiple worlds can flourish—where communities outside of the Center can design their own life projects, which reflect who they are, what they value, and who their ancestors had been. This paper argues that a Pluriversal Design—which aims to nurture alternative models of life and ways of world- making—is necessarily desire-based, in contrast with conventional needs-based approaches. I make a case for Social Design researchers and practitioners to hone our craft to respond primarily to the communities' desire, in contrast with the current focus on their needs, problems and deficits. I argue that needs-based approaches are conservative, implying the reproduction of a model of life —i.e., what is ‘desirable’ is supposedly know from the start and/or externally defined. Such approaches do not encourage the creation of new ‘possibles’. On the other hand, a desire-based approach is open-ended. In terms of creating a world of many worlds, what is the final result? We do not know yet; we will be doing something that has never been done before. Metaphorically, desire-based social change is about trailblazing with the help of a compass. Desire is the force that we have to engage when our task is to create new ways of shaping the human presence on this planet and new ways of world-making—an open- ended process towards the Pluriverse.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".