Learning in action: embedding the SDGs through the Reach Alliance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it