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
<JATS1:p>This book examines the principles and practice of automation in public governance.</JATS1:p> <JATS1:p>Automation is changing the face of government and public law. This collection examines key challenges posed by automation, focusing on theoretical issues, case studies, as well as practices and proposals for reform.</JATS1:p> <JATS1:p>It brings together scholars, public officials and judges from a range of jurisdictions, including the UK, the USA, Australia, Canada, Austria, France and the Netherlands to examine principles that should guide automation in government and what can be learned from the growing policy failures involving automation.</JATS1:p> <JATS1:p>The book contains case studies of significant policy failures involving automation - the Dutch ‘child benefits scandal’, the Horizon accounting software used by the UK Post-Office and Australia’s robodebt social security scandal. These chapters are valuable studies about policy failures involving automation and highlight lessons to be learned.</JATS1:p> <JATS1:p>Making an important contribution to public law, governance and automation, the collection highlights challenges faced by all jurisdictions and draws out lessons from some serious failures of administration involving automation.</JATS1:p>
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it