Bibliographic record
Abstract
In recent years, some cities and localities in the United States, Canada, the United Kingdom, and elsewhere have adopted or intend to adopt one potential solution to the difficulties inherent in addressing the needs of street beggars: diverted giving schemes (DGSs). A DGS is an institutional response designed to motivate people to donate money in charity boxes or donation meters rather than directly to street beggars. Their advocates believe that DGSs are both more efficient and more ethically permissible than direct giving to individual beggars. This article asks whether and how a DGS can be justified. It offers a normative evaluation of the main idea behind this policy, namely, that anonymous and spontaneous donations to charity boxes are in themselves an adequate policy instrument to address the problem of street begging. Ultimately, the paper argues against this idea and develops the case that DGSs can potentially compromise our ability to act on our moral duties toward truly needy beggars. Moreover, it explains why and under which circumstances this kind of program can potentially and seriously interfere with the freedom and opportunities of individuals in the begging population.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".