Learning in action: embedding the SDGs through the Reach Alliance
Bibliographic record
Abstract
Abstract There has been increasing practical and scholarly interest in the engagement of universities with the Sustainable Development Goals (SDGs). However, there has been limited examination of international university collaborations focusing on the SDGs and how they become embedded within universities. Addressing this need, this article explores the experiences of three members of the Reach Alliance a consortium of eight higher education institutions from around the globe. Reach supports students and faculty mentors to study how critical interventions can be made accessible to those who are the hardest to reach. This work aligns with SDG 4 (Quality Education), as well as SDG 17 (Partnership for the Goals) and the Goal’s second universal value of leave no one behind. This commitment to connecting education and societal engagement resonates with Goddard et al.’s work on the civic university as both “globally competitive and locally engaged” (2012: 43). This article focuses on University College London (UK), Ashesi University (Ghana), and Tecnológico de Monterrey (Mexico), selected for their diverse structures and geographies. For each case, we examine how the Reach Alliance initiative has been institutionally embedded, as well as the role of local and global partnerships in making the case for supporting Reach. We find that Reach’s organisation as an international network has encouraged its adoption by host institutions. The initiative’s emphasis on both local concerns as well as the global goal and networks has also resonated with host institutions. This article will be of interest to those working in sustainability and higher education when considering strategies for introducing or increasing SDG-focussed research and teaching.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".