TOWARD INTIMATE DATA: RE-THINKING DIGITAL, SOCIAL, POLITICAL RELATIONS
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
Digital technologies enable the mass datafication of human activity in new and intimate ways, allowing for both active and passive tracking bodily functions, physical movements, consumption habits, social encounters, and even moods and feelings. Despite the seeming newness of these developments, however, Internet scholars recognize that data production and use has always been bound up with broader relations between individuals, communities, and claims to the privateness or publicness of certain bodies, spaces, and behaviors. Most recently, critical data scholars have illuminated the complex, often surreptitious contexts in which datafication occurs, offering a range of conceptual frameworks to contend with the meanings and implications of these deeply personal digital-human entanglements. In this paper we take up and recast the notion of “intimate data.” Elsewhere denoting a particular category of tracked activities deemed private or sensitive, we instead consider intimacy as marking a set of (often unequal) socio-political relationships. That is, we mobilize “intimate data” to attend to the processes by which individuals and collectives are datafied in ways that have repercussions for knowledges about oneself and others. In so doing, we sidestep hermetic liberal conceptions of data that center ideals like consent and exchange to think about data collection as eliciting confessions , vulnerabilities, monetizable practices, and new possibilities for governing (inter)personal and other relations. We advance different, alternative political responses, focusing specifically on (1) the (racialized, gendered, classed, sexualized) normativity of intimate data, (2) (re)considerations of privacy and surveillance, (3) tensions around visibility, and (4) responsibilization of individuals to police spaces.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it