Dominant meanings classification model for web information
Why this work is in the frame
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Bibliographic record
Abstract
The huge amount of information available on the Web can help in building domain knowledge of a Web-based tutoring system. Therefore, we are in need of a way to classify this information at a suitable place. To overcome this challenge, we develop a dominant meanings classification model. This model constructs domain knowledge as a hierarchy of concepts. Each concept consists of some dominant meanings, and each of those is linked with some chunks (information fragments) to define it. The dominant meanings are a set of keywords that best fit an indented meaning of a target word (concept). The more dominant meanings, the better a concept relates to its chunk context. We investigated the effect of using this model to extract features on classifying Web information. We compared the model's results with Naive Bayes classifiers. Our experiment showed that using this approach greatly improves the classification task.
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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 | 0.002 |
| 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