Making data markets: Assetization, valuation, and proxy work in a digital health start-up
Bibliographic record
Abstract
Digital data are increasingly framed as essential resources in health and medicine, implicating diverse actors who work to transform them into different forms of value. In this article we focus on the diverse and contingent valuation practices that shaped an artificial intelligence-enabled 'smart' health technology and the data it generated at different moments in time, and the corresponding asset forms that were envisioned, developed, tested, and marketed. We also outline the role of assetization as a contested but essential design and marketing activity, and introduce the notion of proxy work as an intermediary between data generation and assetization, where people, infrastructures, and other material devices are arranged in such a way that data become capable of 'standing in' for something else, allowing accountable forms of value to be realized across multiple sites. We conclude with a discussion of the consequences of assetization as a dominant lens through which governments, firms, and other actors increasingly understand the value of digital health data, and the different health-related futures those practices make possible.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.002 | 0.013 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".