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
In the past decade, there has been a trend toward using the Internet to support traditional classroom teaching and to substitute traditional teaching for online learning. In particular, online learning communities play an important role in distant education. This trend raises important questions about the nature of online learning, the types of learning promoted by online learning communities, the challenges inherent in online learning communities, and the ways in which online learning communities can be improved. Moreover, it is important to understand online learning communities in the context of people's everyday lives. This special issue brings together studies that examine how online learning communities have evolved, the types of online learning communities available, and what design features are useful for promoting vibrant online learning communities. These studies show online learning communities as a complex phenomenon and propose new frameworks and research methods. This introduction outlines the important questions asked in these papers about online learning communities as well as results from empirical studies. It concludes with a set of design considerations that emerge from the studies.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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