Evaluating profiling as a means of allocating government services
Bibliographic record
Abstract
This paper considers the use of statistical profiling to allocate persons to alternative options within government programs, or to participation or non-participation in programs Profiling has been used in the United States to allocate unemployment insurance (UI) claimants to reemployment services based on the predicted duration of their UI claim. We place profiling in the context of the choice among alternative assignment mechanisms. Different mechanisms have different costs and benefits — any one mechanism, whether profiling or something else, may not be optimal for every program. Within profiling systems, we highlight the need for clarity regarding the objective of the assignment mechanism, e.g. equity or efficiency, and we discuss situations in which equity and efficiency goals may conflict. In relation to UI profiling in the United States, we provide empirical evidence from the state of Kentucky on two important questions. First, we demonstrate that it is possible to effectively predict the duration of UI spells, but that effectively doing so requires using more covariates than many US states presently do. This finding is important because effective prediction of the profiling variable is a necessary but not sufficient condition for the success of a profiling system. Second, we show that the impact of reemployment services does not appear to vary with expected duration of the UI spell, indicating that UI profiling in Kentucky does not advance the goal of efficiency, though it may advance equity goals.
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".