International Collaboration for a Sustainable Future
Bibliographic record
Abstract
This paper represents a concrete reflection on the first steps in a Collaborative Online International Learning journey through the Global Polytechnic Alliance participation in Map the System. The polytechnics are in Denmark (VIA), Canada (Humber College), and New Zealand (Otago Polytechnic). Students and faculty participated in the initiative to work together strategically, based on common interests, to strengthen the participating institutions academically and globally. Three international teams were developed to participate and enter into the Map the System global competition. The teams chose a social or environmental issue that mattered to them and researched connecting elements and factors to share findings in a way that people can meaningfully learn from. This competition, and the paper, is viewed as a discovery process. In this article, we describe three stages the team went through faculty team formation, teaching and learning as well as developing student research and system maps. Through this process, we discovered key insights on creating a sense of community online, systems thinking and reflective learning process. The paper concludes with our thoughts on the unintended gifts of collaborating internationally in virtual teams.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".