Bibliographic record
Abstract
Welcome to the fifth and final issue of 2018.It has been a wonderfully productive and busy year for IRRODL.We have published 88 research articles along with our selection of "notes" from various areas of the field and book reviews.Keep those articles coming (while paying close attention to standards, formatting, and word length!).When I first glance at the listed articles for publication in an issue, wearing my organizational hat, I am looking at their subject matter in order to find some coherence among topics.This choice is usually driven by numbers: for this issue, the numbers surely point to MOOCs.King, Pegrum, and Forsey -all from Australia -consider the state of MOOCs and OER -together constituting a good portion of "visible" open in the Global South.From a literature review, they conclude that the "ongoing tendency for the research literature to pay little heed to the agency of the social actors with the most to gain from these innovations is noted," and they use this reality to call for more research into online learners in the Global South.MOOCs are under study everywhere and in all ways.From Russia, Sablina, Kapliy, Trusevich, and Kostikova examined how MOOC learners perceive success.It is interesting to note that they "discovered that taking MOOCs often coincided with the time when an individual was planning to change career, education, or life tracks."In spite of not receiving formal credit for their studies, learners felt as though they had benefitted from their MOOC experiences.van den Beemt, Buijs, and van der Aalst from the Netherlands and Germany, have also explored learning behaviours and progress in MOOCs.Using the process mining and clustering approach, they identified techniques for successful MOOC completion.Another international team of authors -Khalil, Prinsloo, and Slade -considered the issue of user consent in MOOCS from micro, meso, and macro perspectives based on the examination of four MOOCs from varying contexts.They propose, in conclusion, that there is a need for greater transparency around the implications of users' consent during registration for a course.Cisel's research on MOOCs considers interactions that take place outside of a course, illustrating a mismatch that can exist between course-prescribed and actual tasks.He found that friends and family often share MOOC activities, conceptualizing in-course activity as the tip of the iceberg.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.377 | 0.271 |
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".