Designing Globally Networked Learning Environments Visionary Pedagogies, Partnerships, and Policies
Bibliographic record
Abstract
I would like to begin by thanking the conference organizers for inviting me to join you here today and to share my research on globally networked learning environments. What I share with you here today is what I have learned through my work with many, many colleagues and students for whose collaboration I am deeply grateful. I would also like to acknowledge the support for this work from McGill University in the form of a teaching and learning grant and from the Council for Programs in Technical and Scientific Communication in the form of a research grant. For several years now, my colleagues and I have been studying partnerships and the role of the internet in higher education. More recently, we have focused on faculty innovations in globally networked learning—the visions they create and aspire to, the hard work and passion they put into helping students develop global understanding and make knowledge in new ways, the challenges they have faced, the successes they have celebrated, and the new opportunities for innovation they have created at their universities. As I will discuss in a little while, globally networked learning environments represent an exciting paradigm shift—an innovation in learning that is critical to the development of a global civil society. And while these learning environments are only emerging, they reflect deeper social and technological
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".