Two Years in the Making: Co-Learning Insights from the CSEAR’s Education Community of Practice
Bibliographic record
Abstract
As the imperative to address unsustainability grows, higher education institutions, individual academics, scholarly networks, and professional bodies are calling for sustainability to be (more prominently) embedded in curricula. Over the past 30 years, a strong body of work has been published related to social and environmental accounting education such as textbooks, academic articles, and teaching cases. Yet, the individual and collective endeavours scholars undertake to develop and embed social and environmental accounting education within their respective institutional contexts often remain invisible. Insights may be gleaned thanks to corridor conversations, informal networks, one-off workshops, or panel discussions. To further strengthen capacity to undertake such education, a community of practice approach might help connecting individuals and sharing experiences. This commentary outlines the aims of the CSEAR Education of Community of Practice, the process of establishing and running this community, and offers preliminary reflections following the first two years of its existence. Finally, we consider the next steps in the development of this initiative as means to enhance collective efforts to mobilize social and environmental accounting education to enable a more sustainable society.
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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.052 | 0.074 |
| Scholarly communication | 0.039 | 0.027 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 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".