36. Greening the 2008 STLHE Conference: Sustainability as a Teaching and Learning Challenge
Bibliographic record
Abstract
Conferences are a great way to exchange ideas, communicate research findings, network, and more. However, as a teaching and learning community, we are becoming increasingly aware of the importance of environmental sustainability. The 2008 Society for Teaching and Learning in Higher Education conference organizers chose A World of Learning as its central theme, evoking the challenges and possibilities of internationalization and globalization in post-secondary settings and encouraging presenters to explore the impact of these trends in colleges and universities. As the conference program began to evolve, it became clear that the theme also represented an important opportunity to consider the ecological impact of this annual event. This paper examines the intent, outcomes, and implications of the initiative, illuminating the ways in which initiatives that, at first glance, appear logistical and material-based, are in fact better understood as changes in individual, community, departmental, and institutional values and practices. The potential of projects, such as this one, to function as co-curricular teaching and learning opportunities in promoting institutional change is also explored.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".