Bibliographic record
Abstract
At the end of the long journal process of submission, review, revision and final publication comes the editorial task of ordering the articles in some sort of logical way so as to increase the issue's accessibility and attractiveness to readers.However, as an editor, I ask myself if it matters that I do this.Given that we all approach learning tasks with our own sets of tools and pre-existing knowledge and assumptions, it must be highly unlikely that any two readers will extract the same meaning from whatever article-order is implemented.Years ago, I learned from a colleague that all lists should be prioritized in order of importance or urgency.Although I took that to heart when making my own lists and although that advice has served me well, the journal's playing field does not afford us that type of prioritizing.In this issue, therefore, I have made a division between articles based on topic area; the predominant division separates distance learning articles from OER articles, the latter being presented first.Within the "distance" pieces, I pondered whether to separate macro to micro, teacher/learner, or geographically.I leave it to you to discern whether I managed any of these!And as always, there are the outliers, articles whose topics are so unique within a collection.In this issue, I would thus classify Cunningham's and Koole's articles.Cunningham has used activity theory to conclude that student beliefs and expectations lead to hidden challenges associated with mixing distance and campus-based students.Koole, writing on identity, has described a preliminary study of the kinds of strategies that students draw upon for interpreting and enacting their identities in online learning environments.Her study results indicate that online learners actively employ a variety of strategies in interpreting and enacting their identities.In the OER camp, Oyo and Kalema give us insight into a new era of universal access to higher education in Africa, achievable through MOOCs, but only if initial requirements are met by respective governments.And from Turkey, Kursun, Cagiltay, and Can's findings show that even though the majority of their study's participants' perceptions of OER benefits and their attitudes toward publishing their course materials were positive, Editorial : 15(6) Conrad
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.271 | 0.278 |
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 source (direct Gemma or distilled Codex), 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".