Sampling Strategies and Active Learning for Volume Estimation
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
This paper tackles the challenge of accurately and efficiently estimating the number of relevant documents in a collection for a particular topic. One real-world application is estimating the volume of social media posts (e.g., tweets) pertaining to a topic, which is fundamental to tracking the popularity of politicians and brands, the potential sales of a product, etc. Our insight is to leverage active learning techniques to find all the "easy" documents, and then to use sampling techniques to infer the number of relevant documents in the residual collection. We propose a simple yet effective technique for determining this "switchover" point, which intuitively can be understood as the "knee" in an effort vs. recall gain curve, as well as alternative sampling strategies beyond the knee. We show on several TREC datasets and a collection of tweets that our best technique yields more accurate estimates (with the same effort) than several alternatives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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