Bibliographic record
Abstract
Chapter 3 shows why the contracts model doesn’t work: consent is absent in the information economy. Privacy harm can’t be seen as a risk that people accept in exchange for a service. Inferences, relational data, and de-identified data aren’t captured by consent provisions. Consent is unattainable in the information economy more broadly because the dynamic between corporations and users is plagued with uneven knowledge, inequality, and a lack of choices. Data harms are collective and unknowable, making individual choices to reduce them impossible. Worse, privacy has a moral hazard problem: corporations have incentives to behave against our best interests, creating profitable harms after obtaining agreements. Privacy’s moral hazard leads to informational exploitation. One manifestation of valid consent in the information economy are consent refusals. We can consider them by thinking of people’s data as part of them, as their bodies are.
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 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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".