Assessing the Extent of Randomization Bias in the Canadian Self-Sufficiency Experimenty
Bibliographic record
Abstract
In Canada, a policy aiming at helping single parents on social assistance become self-reliant was implemented on an experimental basis. The Self-Sufficiency Entry Effects Demonstration randomly selected a sample of 4,142 single parents who had applied for welfare between January 1994 and March 1995. It turned out only 3,315 agreed to be part of the experiment despite a 50 % chance of receiving a generous, time-limited, earnings supplement conditional on finding a full-time jobs and leaving income assistance. The purpose of this paper is to determine whether a refusal rate as high as 20 % is likely to bias the measurement of the treatment effect. We compare the estimated impact of the program using experimental data only to those obtained using additional data on individuals not taking part in the experiment. We write the likelihood of various sets of information and obtain relevant estimates of program impact on welfare spell durations. We find strong evidence of randomization bias in the data. When we correct for the bias, we find that estimates that rely on experimental data only significantly underestimate the true impact of the program. y The authors gratefully acknowledge financial support from le Fonds de recherche sur la société et la culture
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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 | 0.082 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".