Basic income guarantee and politics : international experiences and perspectives on the viability of income guarantee
Bibliographic record
Abstract
Introduction: Hopes and Realities of Adopting Unconditional Basic Income Guarantee Schemes R.K.Caputo On the Political Feasibility of the Basic Income Guarantee: Theoretical Considerations J.D.Wispelaere & J.A.Noguera PART I: HOPES The Best Income Transfer Program for Modern Economies E.M.Suplicy An Anniversary Note - BIEN's 25th G.Standing PART II: REALITIES European Union Countries Finland: Institutional Resistance of the Welfare State against a Basic Income M.Ikkala Germany: Far, though Close: Problems and Prospects of BI in Germany S.Liebermann Ireland: Pathways to a Basic Income in Ireland S.Healy & B.Reynolds Netherlands: Final Piece of the Welfare State is Still to Come M.Hasslet Spain: Basic Income from Social Movements to Parliament and Back Again D.Raventos , J.Wark , & D.Casassas PART II: OTHER OECD COUNTRIES Australia: Will Basic Income Have a Second Coming? J.Tomlinson Canada: A Guaranteed Income Framework to Address Poverty and Inequality? J.P.Mulvale & Y.Vanderborght Japan: Political Change after the Economic Crisis Introduces Universalist Benefit T.Yamamori Mexico: The First Steps toward Basic Income P.Yanes United Kingdom: Only for Children? M.Torry United States of America R.K.Caputo PART III: OTHER COUNTRIES Iran: A Bumpy Road toward Basic Income H.Tabatabai
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".