‘This is NOT <i>human</i> services’: Counter-mapping automated decision-making in social services in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper offers a counter-map of automation in social services decision-making in Australia. It aims to amplify alternative discourses that are often obscured by power inequalities and disadvantage. Redden (2005) has used counter-mapping to frame an analysis of big data in government in Canada, contrasting with ‘dominant outward facing government discourses about big data applications’ to focus on how data practices are both socially shaped and shaping. This paper reports on a counter-mapping project undertaken in Australia using a mixed methods approach incorporating document analysis, interviews and web scraping to amplify divergent discourses about automated decision-making. It demonstrates that when the focus of analysis moves beyond dominant discourses of neoliberal efficiency, cost cutting, accuracy and industriousness, alternative discourses of service users’ experiences of automated decision-making as oppressive, harmful, punitive and inhuman(e) can be located.
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.
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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it