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
Active learning can actively select or construct examples to label to reduce the number of labeled examples needed for building accurate classifiers. However, previous works of active learning can only ask specific queries. For example, to predict osteoarthritis from a patient dataset with 30 attributes, specific queries always contain values of all these 30 attributes, many of which may be irrelevant. A more natural way is to ask "generalized queries" with don't-care attributes, such as "are people over 50 with knee pain likely to have osteoarthritis?" (with only two attributes: age and type of pain). We assume that the oracle (and human experts) can readily answer those generalized queries by returning probabilistic labels. The power of such generalized queries is that one generalized query may be equivalent to many specific ones. However, overly general queries may receive highly uncertain labels from the oracle, and this makes learning difficult. In this paper, we propose a novel active learning algorithm that asks generalized queries. We demonstrate experimentally that our new method asks significantly fewer queries compared with the previous works of active learning. Our method can be readily deployed in real-world tasks where obtaining labeled examples is costly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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