Bibliographic record
Abstract
This paper argues that emerging technologies of datafication are intensifying a moralisation of predictivity. On one hand, this describes the growing pressure to quantify and predict every kind of social problem. Reluctance to adopt emerging technologies of surveillance is construed as abdication of a moral responsibility via negligence to inevitable progress. On the other hand, it describes the corresponding demand that human subjects learn to live in more predictable and machine-readable ways, adapting to the flaws and ambiguities of imperfect technosystems. This argument echoes that of Joseph Weizenbaum (1976), a pioneer of early AI research and the inventor of the ELIZA chatbot: that well in advance of machines fully made in our image, it is the human subjects that are asked to render themselves more compatible and legible to those machines. Drawing from a book-length research project into the public presentation of surveillance technologies, I show how messy data, arbitrary classifications, and other uncertainties become fabricated into the status of reliable predictions. Specifically, the bulk of the presentation will examine the rapid expansion of counter-terrorist surveillance systems in 2010’s America. All in all, the moralisation of predictivity helps suture the many imperfections of data-driven surveillance, and provide justificatory cover for their breakneck expansion across the boundaries of public and private. They perpetuate the normative expectation that what can be predicted must be, and what needs to be predicted surely can be. In the process, spaces for human discretion, informal norms, and sensitivity to human circumstance are being squeezed out.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".