‘Find love in Canada’: Distributed selves, abstraction, and the problem of privacy and autonomy
Bibliographic record
Abstract
This chapter will consider the uneasy relationship between what we see as our human rights to privacy and the connected nature of social life in contemporary digital culture. The aim of this chapter is to frame the problem of privacy and autonomy in digital culture, not as a legal or technical problem but as a problem of ethics related to presence – in this case the ethics of an absent presence resulting from the abstraction of information generated from persons. Thus, in the first section, I argue that the notion of abstraction is at the heart of issues around privacy and autonomy in digital culture. I am going to suggest that contemporary digital culture consists of five modes of abstraction: informatization, commodification, depersonalization, decontextualization and dematerialisation. I argue that personal information when treated as abstract ‘data’ can be easily divorced from the person and therefore from ethical obligations associated with personhood, effectively allowing the removal of such information from the social sphere of ethics and morals, making it ethically ‘weightless’. In the second section, I argue that to address the problem of privacy more productively it is worthwhile considering not only what privacy is and what rights humans have to a private life but also what it actually means to be ‘human’ in an era of digital communications and networked environments. It is important to understand how being-in-the-world now necessarily involves the spreading of our presence into a myriad of places and how the increasing virtualization of social life has extracted (and abstracted) our presence and our very being into bits of data which are inevitably free-floating: both beyond our control or even our awareness. As a result, the second section will examine what it is to be a ‘self’ in online culture through Rotman’s (2008) concept of the parallel or quantum self, as well as Stiegler’s (1998) concept of exteriorization. I conclude by suggesting that new consideration needs to be given towards digital or immaterial components of self (i.e. personal data) as matter of being or part of the self, not as ‘representational of’ or ‘information about’ persons. Such a shift in thinking is necessary to give personal data ‘ethical weight’ and thus maintain any prospect of privacy and autonomy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.088 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".