Recalibrating co-design through positionality-in-action: the ReCoDe approach
Bibliographic record
Abstract
This theoretical paper introduces the ReCoDe Approach: Recalibrating Co-Design through Positionality-in-Action. ReCoDe is a critical framework to address a significant gap in participatory design: the absence of structured, team-based approaches to positionality. While design education and practice increasingly emphasise reflexivity and equity, most frameworks treat positionality as an individual exercise, overlooking its relational and systemic dimensions within co-design teams. Drawing on existing literature and a decade of intercultural collaboration through the culturalSYNERGY™ global cyber-charette, this study proposes a dynamic model that embeds four interrelated actions: positionality-in-action, communication, mediation, and team-building – throughout the co-design process. These actions are iterative and intersecting, supporting reflexive and justice-oriented design teams. The overarching objective of the ReCoDe framework is power recalibration: the intentional redistribution of influence, authority, and voice within collaborative design processes. By conceptualising positionality as layered (ascribed, intentional, and co-constructed) and fluid, the model fosters shared understanding, trust, and ethical responsiveness in diverse teams. ReCoDe reframes co-design as a reflexive stance – one that embraces complexity, centres relational ethics, and supports more equitable and transformative design outcomes in both educational and professional contexts.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".