Too poor to say no? Health incentives and disadvantaged populations
Bibliographic record
Abstract
Incentive schemes, which offer recipients benefits if they meet particular requirements, are being used across the world to encourage healthier behaviours.From the perspective of equality, an important concern about such schemes is that since people often do not have equal opportunity to fulfil the stipulated conditions, incentives create opportunity for further unfair advantage.Are incentive schemes that are available only to disadvantaged groups less susceptible to such egalitarian concerns?While targeted schemes may at first glance seem well placed to help improve outcomes among disadvantaged groups and thus reduce inequalities, I argue in this paper that they are susceptible to significant problems.At the same time, incentive schemes may be less problematic when they operate in ways that differ from the 'standard' incentive mechanism; I discuss three such mechanisms.Incentive schemes, which offer recipients benefits if they meet particular requirements, are being used across the world to encourage healthier behaviours.From the perspective of equality, an important concern about such schemes is that when individuals are not equally well positioned to fulfil the stipulated conditions, incentives create opportunity for further unfair advantage.[1]Are incentive schemes that are available only to disadvantaged groups less susceptible to such egalitarian concerns?To the extent that they offer disadvantaged individuals a benefit that will make them better off (usually in terms of resources) on the condition that they do something that will also make them better off (usually in terms of health), such schemes may at first glance seem well placed to help improve outcomes among disadvantaged groups and thus reduce inequalities.However, as I argue in this paper, incentives that target disadvantaged groups are susceptible to significant problems, including concerns about equality.At the same time, incentive schemes can be less problematic when they operate in ways that are not primarily the 'standard' incentive mechanism; I discuss three such mechanisms.
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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 | 0.009 | 0.023 |
| 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 | 0.013 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".