Bibliographic record
Abstract
The normative dimensions of digital health characterise the focus of a growing body of scholarship in the social sciences and humanities. Co-design is one approach that attempts to imbue technology development processes and artefacts with more pluralistic values. While co-design approaches are varied and often pursue different aims, they are increasingly recognised as insufficient to address key challenges associated with the contemporary field of digital health. In this chapter we address three such challenges through a turn to ontology in design and health systems research: anthropocentrism, datafication and digital extractivism, and the agency of design participants. We review these challenges and discuss how a deeper engagement with ontology generates alternative normative commitments for co-design in the context of digital health innovation. Specifically, we engage critically with key concepts in co-design that tend to be taken for granted, and propose a shift in perspective on each of design (orienting toward objects), participation (orienting away from assumptions of the agentic designer), and innovation (orienting away from extractivism). We conclude with reflections on the relation of ontology to normativity in digital health, and suggest practical directions for co-design informed by these considerations.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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.
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".