Developing Shared Distance Education at North Coast University: A Case on the Issues Involved in Using Technology to Enable Syndicated Distance Education
Bibliographic record
Abstract
This fictional case is presented to facilitate discussion of the pros and cons of shared distance education. The key premise concerns whether a syndicate of colleges and universities should collectively offer courses and programs rather than single institutions doing it alone. It describes a number of current efforts and outlines a model to explain the relative lack of sharing. Potential success factors derived from the realm of electronic commerce are presented as a guide for such an effort. The question remaining for students and others is what the pedagogical and business plan should be for a shared distance education initiative. Ce cas fictif est présenté afin de faciliter la discussion entre les pour et les contre du partenariat en formation à distance. L'enjeu principal consiste à savoir si un regroupement de collèges et d'universités devraient offrir collectivement des cours et des programmes plutôt que de le faire sur une base individuelle. Cet article fait état de certaines expériences en cours et propose un modèle pour expliquer l'absence relative de partenariat. Des facteurs de succès inspirés du commerce électronique sont proposés afin de mieux supporter les efforts de partenariat en formation à distance. Ce qui reste à élaborer, pour les étudiants et les autres acteurs du domaine, sont des plans d'affaire et une planification pédagogique pour le succès d'une telle entreprise.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".