An Adaptive Data Collection Procedure for Call Prioritization
Bibliographic record
Abstract
Abstract We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is adaptive in the sense that the effort assigned to a sample unit may vary from one unit to another and may also vary during data collection. The goal of an adaptive procedure is usually to increase quality for a given cost or, alternatively, to reduce cost for a given quality. The quality criterion often considered in the literature is the nonresponse bias of an estimator that is not adjusted for nonresponse. Although the reduction of the nonresponse bias is a desirable goal, we argue that it is not a useful criterion to use at the data collection stage of a survey because the bias that can be removed at this stage through an adaptive collection procedure can also be removed at the estimation stage through appropriate nonresponse weight adjustments. Instead, we develop a procedure of call prioritization that, given the selected sample, attempts to minimize the conditional variance of a nonresponse-adjusted estimator subject to an overall budget constraint. We evaluate the performance of our procedure in a simulation study.
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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 | 0.087 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".