Book review - Economics of distance and online learning: Theory, practice and research
Bibliographic record
Abstract
The book edited by Bramble and Panda provides a comprehensive overview of the organizational models of distance and online learning from an international perspective and from the point of view of planning, costing, management, and decision making.Chapters are written by different experts working in the field.For those not familiar with costing and distance education it provides a valuable introduction to the different facets of handling the economic aspects of distance education and online learning.Experienced scholars will find some interesting viewpoints and methodologies to inspire further discussion and research. Chapter 1: Organizational and Cost Structures for Distance and Online LearningThe chapter written by the editors themselves introduces the term distance education and highlights the generations of distance education according to Taylor.It then goes on to list models of delivery following Peters.Finally general factors are listed that influence costs, such as country characteristics, system design, organizational type, and curriculum focus.The chapter ends with a listing of cost categories and types of cost analysis.(The definition given of cost effectiveness is not shared by the reviewer."A cost effective system is one that minimizes cost and maximizes effectiveness of outcomes."In my view cost can only be minimized for given output; minimization and maximization are not achievable at the same time.)
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".