Locally Universal: Universal Basic Income Policies in the Post-Pandemic World-Order
Bibliographic record
Abstract
Rampant disparities within the capital/labor share, increased pressure on climatically vulnerable communities and mass international migration due to economic hardship or violence. All that without mentioning the ever-haunting specter of automation-induced unemployment and, finally, the outbreak of a world-reaching pandemic: these are some of the ongoing cataclysmic trends that are making an everincreasing number of academics, policymakers and multilateral organizations revisit the adoption of Universal Basic Income (UBI) models. The idea of furnishing guaranteed, unconditional and universal basic income for people within an assigned geographical locality – and potentially the entire globe – has ebbed and flown from the pages of authors of all walks of the political spectrum for over two centuries. It appears, though, that such an idea is regaining momentum at this point in history, a somewhat unexpected moment, given the worldwide rise of nationalistic and illiberalism worldviews. The ambition of this proposal is not to promote an exhaustive comparative assessment of competing proposals currently taking place – or being aspired at – around the world. Instead, this working paper stands as an introductory effort to be followed by a more robust case study of existing schemes, which should bind them under the theories of Multipolarity. This proposal launches the cornerstone of a debate assessing the concrete costs and political coordination challenges that are likely to arise in a scenario of massive and ideally genuine universal effort to start or scale-up existing UBI initiatives through the deployment of digital financing techniques, including its most disruptive variations such as cryptocurrencies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".