Technological Change and the Measure of Redistributive Preferences
Bibliographic record
Abstract
This document presents a pre-analysis plan for a survey experiment to be conducted among nationally representative samples in the United States, Canada, and Japan. The design of the experiment is motivated by previous work in which respondents are exposed to informational treatments about AI and automation, and are subsequently asked about their preferences for redistributive policies. But this growing body of work on technological change and support for redistributive policies provides mixed findings. In this project, we suggest that these conflictual findings are likely due to aspects of experimental design and analysis. We aim to demonstrate that previous studies using survey experiments to examine the effect of technological change on support for redistribution often failed to establish a clear link between the informational treatments provided in the experiment and the outcome questions related to redistribution. As a result, these studies often did not make "support for redistribution" the target attitude. Relying on a survey experiment, we aim to demonstrate that the effect of technological change on redistributive preferences is largely conditional on the design of informational treatments.
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.
How this classification was reachedexpand
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.019 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".