Service-Learning as a niche innovation in higher education for sustainability
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
Education for Sustainable Development (ESD) is a framework proposed by UNESCO to develop knowledge, skills, values, and behaviors in youth for sustainable development. As part of the global development agenda, higher educational institutions are expected to integrate ESD into their curricula. Service-Learning is a type of experiential learning in which students combine academic coursework with community service which is aligned with the learning objectives of their academic program. In light of the global trend, our paper investigates how universities are responding to this call through the introduction of Service-Learning programs. First, a comprehensive review of UN documents presents the background and structure of ESD. Second, a systematic review of the academic literature analyses how Service-Learning is being introduced in higher educational institutes. Key findings are that Service-Learning programs align with most of the UNESCO framework components, but higher education institutions are finding it challenging to implement them. Educators play a pivotal role in implementation, and unless they are trained and incentivized and this is systematized, not only Service-Learning but also ESD may fail to transform learning environments. Furthermore, there is a need for impact evaluation, particularly in terms of key sustainability competences. The three major challenges are insufficient educator capacity, funding, and educator attitudes. These challenges can be addressed through university-based projects addressing local problems that have a visible impact, as well as collaboration with local communities, other institutions and, social enterprises.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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