Bibliographic record
Abstract
Despite its mainstreaming into the broader educational ecology, open and distance education (ODE) still leaves much to be desired in terms of both practice and research. Inspired and informed by the author’s 35 years of experience as an ODE practitioner, researcher, reviewer, and editor, this article concentrates on 10 critical issues of ODE research that have long existed but may have a consequential impact on its healthy growth. The issues discussed cover scarcity of longitudinal research, paucity of scaling-up and generalization research, preference for success over failure presented in research, the need for a systems approach, lack of sociocultural sensitivity, technologization of research, scant attention to ODE for the underprivileged and disadvantaged, insufficient research on ODE policy, negligence of historical research, and disinterest in revisiting ODE theories. The causes of these problems are critically interpreted and their possible negative impacts on the field of ODE are explored in a concise manner. The purpose of this article is to encourage further discussion and debate on ODE research to sustain its presence and acceptance as a legitimate mode of education in the wider educational community.
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 | 0.445 | 0.614 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.015 | 0.097 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".