Guided Learning of Human Sensor Models with Low-Level Grounding
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
Sensor data often lacks intuitive interpretability in its raw form, unlike language or image data. Furthermore, standard end-to-end training leaves little control over local representation learning. We postulate that guided local representation learning could be used to tackle both issues. In this paper we introduce a novel framework for sensor models which uses low-level grounding for guided learning of human sensor models. Our framework is amenable to different model architecture. We demonstrate our method on two different human activity datasets, one containing labels of low-level actions used in performing high-level activities, and one without any low-level labeling. We provide comprehensive analysis of our frameworkâs performance across many low-level action subsets and demonstrate how it can be easily adapted to data with no low-level labeling. Our results demonstrate that low-level grounding can be used to improve both the interpretability and performance of sensor models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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