Bibliographic record
Abstract
Introduction PART I: EXPERIMENTS The Basic Income Guarantee in the United States: Past Experience, Current Proposals K.Widerquist & A.Sheahan Seeing the Sun Rise: The Basic Income Grant Pilot Project in Namibia - Realities and Hopes C.Haarmann & D.Haarmann Minimum Income in Brazil: A New Model of Innovation Diffusion D.B.Coelho The Case for Basic Income in Canada E.L.Forget PART II: PROPOSALS Basic Income and Republican Freedom in North and South: Financing Proposals for Catalonia and East Timor D.Cassassas , J.Wark & D.Raventos The Continuing Politics of Basic Income in South Africa J.Seekings & H.Matisonn Ireland and the Prospects for Basic Income Reform S.Healy & B.Reynolds Manifold Possibilities, Peculiar Obstacles - Basic Income in the German Debate S.Liebermann Prospects for a Tax-Benefit Reforms in New Zealand that Incorporates a Basic Income K.Rankin Basic Income in Australia - A Distant Horizon J.Tomlinson Conclusion
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.006 |
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".