Playing with Power Tools: Design Toolkits and the Framing of Equity
Bibliographic record
Abstract
Design toolkits that aim to to promote equity offer designers simplified approaches to creating more equitable technology. However, it is important to understand how equity is conceptualized in practice. As a curated collection of methods, toolkits signal how equity is imagined in design. In this paper, we perform a qualitative analysis of 17 design toolkits related to equity. We explore alternative design approaches that address inequity in design. We evaluate whether equity toolkits align with calls for changes to design practice, as well as Nancy Fraser’s dimensions of justice. Finally, we find that design toolkits focus on the ‘digital divide’ rather than redistributing world-building power, and thus continue to keep design power with professional designers. We also find that ‘design thinking’ continues to influence design toolkits. Furthermore, the simplicity of toolkits does not engage with the complexities that shape equity in practice. We conclude with suggestions to help researchers and designers rethink design toolkits.
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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 | 0.103 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.094 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| 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".