Bibliographic record
Abstract
This article examines the “digital welfare state” historically, presently, and into the future, with a focus on what artificial intelligence means for welfare surveillance. Drawing on scholarship about the development of bureaucracy, the welfare state, and automation, as well as specific examples from the Netherlands, I argue that problems posed by artificial intelligence in public administration are often misplaced or misattributed and that the societal challenges we can expect to encounter in welfare surveillance are more likely to be historically familiar than technologically novel. New technologies do provide some new capabilities, which explains the uptake of algorithmic tools in welfare fraud investigation and the use of chatbots in assisting with welfare applications. Algorithmic systems are also increasingly subject to “audits” and regulations that mandate accountability. However, many of the key issues in the automation of the welfare state are the same as identified in scholarship that long precedes the current hype around artificial intelligence. These issues include a persistent suspicion of welfare recipients to justify surveillance as a form of fraud identification, opaque decision-making, and punitive measures directed against marginalized groups, enacting harm and reproducing inequalities.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".