adopting institutions, including MIT, John Hopkins, and Open Universiteit Nederland.
Bibliographic record
Abstract
IRRODL continues to grow and succeed, and we wish to thank those whose time, energy, and expertise have contributed to this success through reviewing one or more articles in the past year. As usual, this issue of IRRODL features articles from around the world, bringing you cur-rent results of research in theory and practice related to a growing number of models, de-signs, and research methods that are evolving as formal education embraces openness. It is exciting times for educational researchers, but more importantly this issue contains ideas that can be used to enrich open learning and teaching everywhere. In the following section, I provide a very brief overview of the articles you will find in this issue. Online constructivist pedagogies are often focused on learning achieved through group projects done collaboratively. The results can be encouraging, but the challenges and levels of adoption and participation vary greatly. A Canadian study, “An Investigation of Collabo-ration Processes in an Online Course: How do Small Groups Develop over Time?, ” applies group development models to formal education groups online and suggests a theoretical model to help explain, understand, and guide teacher and student behavior when engaged in collaborative activities. We are all trying to figure out business models for open content development and deliv-ery, especially given the recent flurry of interest in MOOC models of free programming. In an international article the authors assess the “Impact of OpenCourseWare Publication on Higher Education Participation and Student Recruitment, ” as demonstrated by early
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.306 | 0.192 |
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; the direct Gemma label and the distilled Codex classifier 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".