Principal Stratification: A Broader Vision
Bibliographic record
Abstract
Recently, Pearl [1] challenged researchers to comment on whether the principal stratification framework (PS) is an objective or a tool. A series of commentaries and responses ensued [2–6] that have heightened interest in the approach originally proposed by several authors [7, 8], and later more formally defined by Frangakis and Rubin [9]. In this brief article, we address the specific issue of compliance in experimental studies. The PS literature uses the taxonomy “compliers” to refer to participants who would follow the treatment assignment under all treatment arms (a baseline characteristic). However, the clinical literature uses “compliers” to refer to participants who did follow assigned treatment. To minimize confusion, we will use “adherence” or “adherers” [10–13] to refer to observed concordance between assigned and observed treatment, and “Baseline Compliers” to refer to those participants with baseline characteristics that would follow assigned treatment regardless of which treatment assignment they received. [...]
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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 | 0.064 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.023 | 0.048 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".