Bibliographic record
Abstract
Foreword Nicholas Timmins Introduction Julia Unwin 'Social evils' and 'social problems' in Britain since 1904 Josie Harris Section 1: Public voices Uneasy and powerless: findings from the online consultation Beth Watts and David Utting Truncated opportunities: eliciting unheard voices on social evils Alice Mowlam and Chris Creegan Living with social evils: further views from people in disadvantaged groups Chris Creegan, Martha Warrener and Rachel Kinsella Section 2: Viewpoints Preface David Utting A decline of values Has there been a decline in values in British society? Anthony Browne Social evils and social good A.C. Grayling Unkind, risk averse and untrusting: if this is today's society, can we change it? Baroness Julia Neuberger Distrust What and who is it we don't trust? Shaun Bailey Fear and distrust in 21st century Britain Anna Minton The absence of society The absence of society Zygmunt Bauman Individualism A wrong turn in the search for freedom? Neal Lawson Individualism and community: investing in civil society Stephen Thake Inequality Opportunity and aspiration: two sides of the same coin? Chris Creegan Five types of inequality Ferdinand Mount The poor and the unequal Jeremy Seabrook Section 3: Reflections Reflections on social evils and human nature Matthew Taylor Afterword David Utting
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.095 | 0.018 |
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".