Tell Me More! Soliciting Reader Contributions to Software Tutorials
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
Online software tutorials help a wide range of users acquireskills with complex software, but are not always easy to follow.For example, a tutorial might target users with a high skill level,or it might contain errors and omissions. Prior work has shownthat user contributions, such as user comments, can add value to atutorial. Building on this prior work, we investigate an approachto soliciting structured tutorial enhancements from tutorialreaders. We illustrate this approach through a prototype calledAntorial, and evaluate its impact on reader contributions through amulti-session study with 13 participants. Our findings suggest thatscaffolding tutorial contributions has positive impacts on both thenumber and type of reader contributions. Our findings also pointto design considerations for systems that aim to supportcommunity-based tutorial refinement, and suggest promisingdirections for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 it