The contradictions of individualized activation policy: Explaining the rise and demise of One to One Service in Australia
Bibliographic record
Abstract
Recent research has documented a trend towards the individualization of activation policies. This study reviews a radical Australian effort to personalize the delivery of activation policies called the One to One Service initiative, which was abandoned shortly after its introduction. The paper asks: what does the brief and tumultuous tenure of this initiative reveal about the role of the individual in contemporary welfare administration and activation policy? Based on a theoretical engagement with the works of Ulrich Beck, Elizabeth Beck-Gernsheim and Anthony Giddens, and an ethnographic study of frontline interactions in Centrelink, Australia’s national benefits agency, the paper argues we should understand the rise and fall of One to One Service in terms of contradictions between two competing forms of contemporary welfare individualization, referred to as ‘democratic relationships’ and ‘compulsory choice’. One to One Service represented a push for individualized, democratic relationships between staff and clients; yet it was undermined because policy makers chose to emphasize an alternative form of individualization premised on forcing recipients to take responsibility for their lives and move off welfare. The findings have implications for the individualization, street level bureaucracy, and activation policy literatures.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.056 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".