The Remote Networked School (RNS) Model: An ICT Initiative To Keep Small Rural Schools and Their Local Community Alive
Bibliographic record
Abstract
The importance of peer interaction for learning purposes is a well-known fact in educational theory, and a school of a small size is particularly challenged to engage same-age students in social exchange of this nature. For almost a decade, an action research partnership (Laferrière & Breuleux, 2002) has been established. A systemic approach was applied (Banathy, 1991; Engeström, 1999; Seidel & Perez, 1994). It has meant tackling an educational challenge and social one as well as distance from urban areas using the support of the Internet. Partners’ objective was to design and study (see design experiment methodology: Brown, 1992; Collins, 1992; 1999), from an ecological perspective (Nardi & O’Day, 1999). The model that was cocreated was meant to enrich interactions for learning purposes in rural schools. More concretely, with the use of information and communication technologies (ICTs), we designed a model whose purpose is to bring classrooms of different schools and regions to work and learn together. This paper focuses on two poles of results of the Remote Networked School (RNS) model: 1) the advantages of collaboration between schools from teachers’ point of view; 2) parents’ social representations of the RNS model and its value as it pertains to their children’s education.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".