Bibliographic record
Abstract
<p>Open and distance learning (ODL) is increasingly being regarded as a viable policy option for developing countries with limited educational resources for buildings, books and trained teachers, seeking to increase accessibility for large numbers of learners in education and training opportunities. Advocates of ODL as an appropriate solution to development issues tend to emphasise the hardware and software (curricula, materials and media of instruction and delivery, and especially ICTs) rather than the learning support needed (See, for example, World Bank, 2002).</p> <p>In one sense this should not be surprising. As Lentell has noted, tutoring has never been at the forefront of mainstream writing on distance education, at least not until fairly recently (Lentell, 2003). However, whilst tutoring might not be central to the writing about ODL in the north, the practice is somewhat different. Tutoring tends to be the less visible element of ODL, but it is no less essential than good materials and effective administration. Distance education cannot exist without tutors who provide feedback and guidance to students. This point is well demonstrated by, for example, the array of institutional handbooks on tutoring produced by distance education universities. In practice, established distance education providers typically invest considerably in tutoring and other forms of learner support (Rumble, 1997). Moreover, and certainly among learner support professionals, there is an implicit "preferred" model. This model assumes a relatively low student-to-tutor ratio, with the tutor offering proactive individual guidance and feedback. Such a model, however, is not easily transferable to a situation where the reasons for adopting distance education are limited numbers of teachers and limited access to educational provision. </p>
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 | 0.045 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".