Bibliographic record
Abstract
A reconceptualization of education for sustainability and global citizenship education (GCE) is proposed, considering evidence from the United Nations decade of education for sustainable development (ESD) and from research with policymakers and adult educators in Wales. In this reframing, global citizenship education is foregrounded, and the model is underpinned by an ecological ethos, where webs of interconnections are highlighted. The model is informed by critical and holistic adult education, and it includes a focus on relational learning and on the affective domain, where emotions are recognised and valued alongside the rational and cognitive. These elements are supported by an ethic of care, which is introduced as a starting point for making what can appear as abstract concepts or remote issues, immediate and relevant to learners’ lived experience. The synthesis of the various theoretical perspectives embodies an inclusive ‘ecological global citizenship education’, where educators and learners are supported to engage with difficult and emotive topics. Dialogue is proposed as the method at the centre of a pedagogy that is critical and humanistic, and that facilitates and supports the often-uncomfortable learning as we honestly and critically examine ourselves and our world within a learning community.
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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".