The Reality of Data Mining: Sculpting Discourse, Knowledge, and the New Subject
Bibliographic record
Abstract
Debates over privacy are especially common in the digital age. They often materialize into attitudes of indifference with moralist cores purporting the question’s irrelevance to the good, law-abiding citizen. While there are plenty of arguments to oppose this position on surveillance, this paper focuses on a different concern in the privacy debate—data mining. In this paper, I argue that data mining—that is the collection of information on the individual such as preferences, locations, emotions, interests, behaviour, demographic, etc.—has concrete effects on our realities. It does so by curating what is sensible and intelligible through discourse, through proxies that culturally embed “truths,” and by constructing new subjectivities. Contrary to the position articulated above, I argue we should care deeply about privacy over our data and scrutinize the normalization of its being collected as a by-product of our participation on web 2.0, smart devices, and an ever-growing digital life.
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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 | 0.113 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.024 | 0.210 |
| Scholarly communication | 0.041 | 0.066 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".