Which Clients are Deserving of Help? A Theoretical Model and Experimental Test
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Street-level bureaucrats have to cope with high workloads, role conflicts, and limited resources. An important way in which they cope with this is by prioritizing some clients, while disregarding others. When deciding on whom to prioritize, street-level bureaucrats often assess whether a client is deserving of help. However, to date the notion of the deserving client is in a black box as it is largely unclear which client attributes activate the prevailing social/professional category of deservingness. This article, therefore, proposes a theoretical model of three deservingness cues that street-level bureaucrats employ to determine whom to help: earned deservingness (i.e., the client is deserving because (s)he earned it: “the hardworking client”), needed deservingness (i.e., the client is deserving because (s)he needs help: “the needy client”), and resource deservingness (i.e., the client is deserving as (s)he is probably successful according to bureaucratic success criteria: “the successful client”). We test the effectiveness of these deservingness cues via an experimental conjoint design among a nationwide sample of US teachers. Our results suggest that needed deservingness is the most effective cue in determining which students to help, as teachers especially intend to prioritize students with low academic performance and members of minority groups. Earned deservingness was also an effective cue, but to a lesser extent. Resource deservingness, in contrast, did not affect teachers’ decisions whom to help. The theoretical and practical implications of our findings for discretionary biases in citizen-state interactions are discussed.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it