School Leadership for Sustainable Development: A Scoping Review
Bibliographic record
Abstract
Sustainable development within educational institutions encompasses an array of objectives as outlined in Agenda 21 (United Nations [UN], 1992). We recognize in this paper that there are cognate terminologies in the field of sustainable development: Education for Sustainable Development (ESD), Education for Sustainability (EfS), Development Education (DE), and Sustainability Education (SE). As stated in the Education for Sustainable Development toolkit (McKeown, 2002), ESD is the terminology most often employed within UN documents; hence, we also employ ESD because it is the term utilized by UNESCO and at the international level. Thus, we avoid the many debates about these terminologies in this paper. The fundamental interest of this review is to assess the current status of school leadership for sustainable development in the K-12 context. With the help of a scoping review, three literature databases were combed to achieve this purpose. The findings reveal school leaders’ perceptions of sustainable development as well as their motives for engaging in ESD. Our analysis indicates that school leaders vaguely understand the term ‘sustainable development,’ and they interpret ESD from the lens of the environment and society. Thus, the economy component of ESD may be de-emphasized in implementation efforts.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".