Strategies for Dealing with the Problem of Non-overlapping Units of Assignment and Outcome Measurement in Field Experiments
Bibliographic record
Abstract
Researchers conducting field experiments are sometimes faced with the challenge of analyzing field experiment results when the unit of assignment does not coincide with the unit of outcome measurement. For example, in electoral research, election results may be reported at a level of geography defined by electoral law, while the assignment of treatment can be made only at a level of geography different from this. Using examples from field experiments conducted in Canada and Mexico, we describe this problem and its consequences for analysis and interpretation of field experiment data and results. We also offer a number of practical solutions analysts can employ when faced with non-overlapping units of assignment and outcome measure in field experiments.
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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 | 0.511 | 0.715 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.014 | 0.015 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".