Surveillance Capitalism, Datafication, and Unwaged Labour: The Rise of Wearable Fitness Devices and Interactive Life Insurance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it