Surveillance Capitalism, Datafication, and Unwaged Labour: The Rise of Wearable Fitness Devices and Interactive Life Insurance
Bibliographic record
Abstract
This paper examines the relationship between interactive life insurance companies and their policyholders and the way in which wearable fitness devices are deployed by these companies as data-generating surveillance technologies instead of personal health and fitness devices. Working within an expanded framework of “surveillance capitalism” (Zuboff 2015), I argue that while the notion of self-care generally associated with wearable fitness devices is underpinned by neoliberal constructs, the incentivization of interactive life insurance programs works to obscure the immense value placed on information capital. This paper briefly considers the legal loopholes involved in the harvesting of sensitive health and fitness information from consumer wearables and suggests that the push toward fitness trackers has little to do with any real concerns for the health and fitness of consumers and policyholders. Lastly, I consider different forms of unwaged labour in the relationship between policyholders and interactive life insurance programs. I contend that policyholders do not recognise the free and immaterial labour that goes into sustaining the data-based business model that interactive life insurance companies and social media platforms use and rely on for profit. In so doing, they relinquish power and control over the data they work to produce, only so that the information can be commodified and used against them.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".