A critical approach to SDGs through Collaborative Online International Learning: experiences from Canada and Spain
Bibliographic record
Abstract
Introduction The Collaborative Online International Learning (COIL) is described as a pedagogical experience linking the classrooms of two or more higher education institutions across culturally and linguistically differentiated regions. COIL intends to provide academics and students with the ability to communicate and collaborate with peers internationally through online interactions. During the Fall of 2021, this collaboration involved the Universitat Jaume I (UJI), located in the city of Castelló de la Plana in Spain and Algoma University, located on the traditional territory of the Anishinaabek Nation, as well as the homelands of the Métis Nation. The student profiles, each institutions’ geographical locations, as well as the linguistic, cultural and critical approaches to teaching and learning provided ample opportunities and challenges for the development and implementation of this experience. Methods The purpose of this paper is to provide a pedagogical reflection on the development of a 5-week module taught together during three academic years. The authors provide an account and reflection of their collaboration centered on the institutional challenges and opportunities that currently exist for courses that aim to engage Indigenous and critical/ecological thinking. Results and discussion The background of the collaboration leads to a detailed analysis of the context of COIL implementation and the reflections on the modules’ development and improvement. Our recommendations are based on lessons learned from the substantive and technical challenges and opportunities on the complexity of teaching about contemporary ecological/social issues, as well as the tools required to inspire future activists.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".