Bibliographic record
Abstract
This dissertation advances a novel government program that could remedy inadequate access to credit for unbanked and underbanked individuals – those it defines as the “very poor.” It sets out the socioeconomic circumstances that create singular barriers for the very poor. It analyses the credit needs of the very poor, the unique institutions they interact with to meet these needs, and the ways in which these institutions intertwine extreme poverty, credit, and marginalisation. The dissertation proceeds to examine the role of the state in the provision and regulation of credit, and in the entrenchment of extreme poverty. It provides a sustained historical analysis of the role of the postal service, a public institution, in the provision of banking and credit and discusses a number of analogous programs and proposals that normalise and contextualise its novel government program. The dissertation extends a framework drawn from antitrust law to argue that state intervention in the marketplace is best understood as falling along a spectrum, from the provision of a competing product or service to the monopolisation of an entire industry. This framework elucidates how we justify state intervention with respect to certain essential, “public” products and services. The dissertation closes with a detailed proposal for a government program that would provide credit to the very poor through loans repaid through additional, progressive taxation. Individuals whose income does not reach a certain level would not need to repay the loan, whereas those with a high income would effectively repay a multiple of the loan principal amount. Repayment would depend on income, but only for a limited period of time. The program may have unique potential to alleviate persistently lower social mobility for the very poor.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".