Bibliographic record
Abstract
Code examples are of great value to programmers trying to learn an unfamiliar API. Effective code examples are often surrounded with plain text explanations of the relevant concepts, techniques, and API elements involved in the example. However, authoring concise yet complete explanations is a challenging balancing act. To address this challenge, we propose Casdoc, a novel authoring technique and presentation format for annotated code examples. Casdoc-formatted code examples are HTML documents designed to embed unobtrusive explanations into the code. They thus contain more explanations to address the varying needs of a larger audience, without disrupting individual readers with information they already know. Explanations are split into short annotations and organized into an intuitive tree-like structure, thus supporting a streamlined authoring process. We used Casdoc to produce 105 Java code examples as part of the course material for an undergraduate computer science course. Students preferred the new format over traditional code examples. Their interaction with code examples suggests that the intuitive structure of Casdoc annotations reduces the need for navigation aids such as search fields.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".