The Sensitivity of Impact Estimates to Data Sources Used: Analysis From an Access to Postsecondary Education Experiment
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
BACKGROUND: This article reports on the Future to Discover Project-a Canadian randomized controlled trial of two high school interventions-where data on key postsecondary enrollment outcomes were collected for two phases. During the initial phase, outcomes were recorded from administrative data and follow-up surveys. During the later phase, data came from administrative records only. OBJECTIVES: The article provides analyses that are informative about the consequences of a change from administrative-only data to survey-only data (and vice versa) for the estimation of impacts. RESULTS: The change from administrative-only to survey-only data tended to produce apparent drops in postsecondary enrollment rates that varied by subgroup and education outcome. Nonetheless, levels and significance of impact with respect to postsecondary enrollment remained relatively stable. CONCLUSIONS: The findings of the article provide evidence that estimating education program impacts in the context of a randomized experiment can be relatively robust to the data sources chosen. They suggest that internal validity and conclusions for policy need not be affected by changing data sources even when the change produces marked changes in levels of the outcome of interest observed.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it