Bibliographic record
Abstract
The idea of a Universal Basic Income (UBI)-—periodic and uncon- ditional cash payments to all citizens—has gained renewed attention amid growing concerns about technological unemployment in advanced economies. More recently, economists have made the case for a UBI in the developing world, where cash transfers distributed to all citizens, rich and poor, may cut through layers of red tape and lead to outsize gains in poverty reduction. In India, a rapid expansion of direct cash transfers linked to the national biometric database and small basic income experiments have galvanized an extensive debate on a UBI. Supporters claim that no-strings-attached payments will be an effective antidote to India’s underperforming antipoverty programs and leaky, distortionary subsidies. Critics worry that they will undermine an already-fragile social security architecture, cause workers to drop out of the labor force, and encourage wasteful spending.Rather than relying exclusively upon the survey’s proposed methods for financing, targeting, and distributing a UBI, Indian policymakers should join their Finnish and Canadian counterparts in running one or several large-scale experimental evaluations. By determining the impact on both the government (state and fiscal capacity) and citizens (economic and social outcomes), such trials can generate new empirical evidence to inform the growing UBI debate and reveal the most effective role for unconditional transfers in India’s welfare architecture.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".