Cybergenre: automatic identification of home pages on the web
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
The research reported in this paper is part of a larger project on the automatic classification of web pages by their genres. The long term goal is the incorporation of web page genre into the search process to improve the quality of the search results. In this phase, a neural net classifier was trained to distinguish home pages from non-home pages and to classify those home pages as personal home page, corporate home page or organization home page. In order to evaluate the importance of the functionality attribute of cybergenre in such classification, the web pages were characterized by the cybergenre attributes of 〈content, form, functionality〉 and the resulting classifications compared to classifications in which the web pages were characterized by the genre attributes of 〈content, form〉. Results indicate that the classifier is able to distinguish home pages from non-home pages and within the home page genre it is able to distinguish personal from corporate home pages. Organization home pages, however, were more difficult to distinguish from personal and corporate home pages. A significant improvement was found in identifying personal and corporate home pages when the functionality attribute was included.
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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 | 0.001 | 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