Telelearning and the education of rural students in Newfoundland and New Zealand. / Télé-enseignement et éducation en milieu rural en Nouvelle Zélande et à Terre Neuve.
Bibliographic record
Abstract
In the Atlantic Canadian province of Newfoundland and Labrador and in New Zealand, new educational structures for the delivery of education to rural students have been developed. By linking small rural schools with one another through the Internet and other technologies, teaching and curriculum resources can be shared and, accordingly, educational opportunities for young people are enhanced. An outcome of the construction of digital intranets and, within these, the creation of virtual classes in both Canada and New Zealand has been an extension of the range of subjects that can be provided in small schools in rural communities. In selected sites in rural Newfoundland, students have been able to study University-level Mathematics, Chemistry, Biology and Physics within virtual classes in a digital intranet while attending schools in their small communities. In rural New Zealand, the range of subjects available to students in rural communities in which schools interface with one another to share resources has been considerably expanded. In both countries, these new educational structures have enhanced educational opportunities for people whose homes are located well beyond major centres of population.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".