The citizen's stake : exploring the future of universal asset policies
Bibliographic record
Abstract
Introduction: The new politics of ownership ~ Will Paxton and Stuart White Part one: Financing a citizen's stake: Inheritance tax: what do the people think?: evidence from deliberative workshops ~ Miranda Lewis and Stuart White Towards a citizens' inheritance: reforming inheritance tax ~ Dominic Maxwell Using stakeholder trusts to reclaim common assets ~ David Bollier Land tax: options for reform ~ Iain McLean A capital start: but how far do we go? ~ Howard Glennerster and Abigail McKnight Part two: Forms of citizen's stake: Attitudes of young people towards capital grants ~ Andrew Gamble and Rajiv Prabhakar Universal capital grants: the issue of responsible use ~ Will Paxton and Stuart White Caretaker resource accounts for parents ~ Anne Alstott Carework: are care accounts the answer? ~ Jane Lewis Having the time for our life: re-working time ~ Linda Boyes and Jim McCormick Conclusion: what is the best way forward for the citizen's stake? ~ Nick Pearce, Will Paxton and Stuart White.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".