Scale dichotomization reduces customer racial discrimination and income inequality
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
Online platforms are rife with racial discrimination1, but current interventions focus on employers2,3 rather than customers. We propose a customer-facing solution: changing to a two-point rating scale (dichotomization). Compared with the ubiquitous five-star scale, we argue that dichotomization reduces modern racial discrimination by focusing evaluators on the distinction between ‘good’ and ‘bad’ performance, thereby reducing how personal beliefs shape customer assessments. Study 1 is a quasi-natural experiment on a home-services labour platform (n = 69,971) in which the company exogenously changed from a five-star scale to a dichotomous scale (thumbs up or thumbs down). Dichotomization eliminated customers’ racial discrimination whereby non-white workers received lower ratings and earned 91 cents for each US dollar paid to white workers for the same work. A pre-registered experiment (study 2, n = 652) found that the equalizing effect of dichotomization is most prevalent among evaluators holding modern racist beliefs. Further experiments (study 3, n = 1,435; study 4, n = 528) provide evidence of the proposed mechanism, and eight supplementary studies support measurement and design choices. Our research offers a promising intervention for reducing customers’ subtle racial discrimination in a large section of the economy and contributes to the interdisciplinary literature on evaluation processes and racial inequality. Changing from a five-point scale to a two-point scale for rating workers reduces racial discrimination by making customers focus on whether the work was good or bad instead of their own personal biases.
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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