Testing perfect rankings in ranked‐set sampling with binary data
Bibliographic record
Abstract
Abstract In ranked‐set sampling, the rankings may be either perfect or imperfect. Statistical procedures that assume perfect rankings tend to be more efficient than procedures that do not assume perfect rankings when perfect rankings actually hold, but may perform poorly if the rankings are imperfect. Several procedures have been developed for testing the null hypothesis of perfect rankings, but these procedures break down if the data are not continuous. In this article, we develop tests of perfect rankings that can be applied with binary data. Motivated by new theoretical results about how the success probabilities in the judgment strata differ under perfect and imperfect rankings, we develop a consistent test with a test statistic that is asymptotically normal. We find, however, that the test does not properly control the type I error rate with small samples. This motivates us to instead implement a bootstrap version of the test. This bootstrap test controls the type I error rate even with small sample sizes. Functions for implementing both tests using R are available in the Supplementary Material. The Canadian Journal of Statistics 45: 326–339; 2017 © 2017 Statistical Society of Canada
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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 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".