Review of Hintz, Dencik, and Wahl-Jorgensen's Digital Citizenship in a Datafied Society
Bibliographic record
Abstract
In our research community, the citizen's digital agency is met with healthy skepticism.At the nexus of surveillance capitalism (Zuboff 2019), surveillance culture (Lyon 2017), and surveillance realism (Dencik and Cable 2017)-an intersecting place propagated by an increasingly closed Internet coding infrastructure (Lessig 2006)-resistance seems futile.So much so, that the notion of even masking one's location and identity is perhaps more performative than pragmatic (Monahan 2015).In a world of rhizomes in which governments piggyback corporations to monitor populations, what exactly does agency look like, and is it possible to reason this way inside the conventional intellectual confines of digital citizenship scholarship?The day I began this review is the day Mozilla announced Track THIS!It is one of a dozen initiatives undertaken by the company to position users to have more control over the who, what, and how of their data.This latest initiative combines education about which companies are mining data from cookies inside their devices, along with a strategy for stopping it.As social scientists, we have suspected cookie technologies to be highly problematic for user privacy (Shah and Kesan 2009;McStay 2012; Lyon 2015; Cooke 2016) -suspicious for a long time, indeed (Bennett 2001; Haggerty and Gazso 2002;Elmer 2003).And we have remained healthily skeptical about the extents of resisting them.But here is Track THIS!, a technology that opens one hundred browsing tabs at once.In doing so, it gives cookies poor ingredients: noisy metadata.It makes the user appear to be someone they are not.Is the premise of resistance technology naïve, or is it meaningful in the context of agency?Do these questions even matter if the privacy ship set sail long ago?
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".