A Note on Sampling and Estimation in the Presence of Cut-Off Sampling
Bibliographic record
Abstract
Cut-off sampling consists of deliberately excluding a set of units from possible samples selection, forexample if the contribution of the excluded units to the total is small and if the inclusion of these unitsin the sample selection involves high costs. If the characteristics of the excluded units differ from thatof the population under study, the use of naïve estimators may result in strongly biased estimations. Inthis paper, we discuss the use of auxiliary information to reduce the non-response bias by means ofcalibration or balanced sampling techniques. It is demonstrated that the use of both the availableauxiliary information related to the variable of interest and of the available auxiliary informationrelated to the probability of response enables to strongly reduce the estimation bias. A short numericalstudy supports our findings.
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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 | 0.144 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".