Digital Open Annotation with Hypothesis: Supplying the Missing Capability of the Web
Bibliographic record
Abstract
Hypothesis is an open-source technology developed by a non-profit organization that enables a conversation across all the content on the Web, a rich and interconnected exchange that goes well beyond experiments with past commenting tools. The publication of annotation as a Web standard by the World Wide Web Consortium in February 2017 paves the way to bring digital annotation natively to browsers, fulfilling an original vision for networked information laid out by Vannevar Bush in 1945. Publishers are exploring annotation to facilitate post-publication discussion layers, add author or expert commentary as supplemental content, and streamline the peer-review process. Researchers are using annotation for fact verification, entity extraction, precise citation, and preprint collaboration. Annotation technology enables the creation of unique persistent Web addresses that are much more precise than page-level URLs, thus opening up new workflow possibilities in scholarly communications and beyond.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| 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 | 0.041 | 0.083 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".