Ecoliteracy: Foundation of Vocationally Specific Sustainability Education
Bibliographic record
Abstract
There is ample evidence that human actions are continuously and progressively straining planetary processes and systems vital for the wellbeing of the planet, as well as people themselves (WWF, 2012, 2014). Education has a key role to play in addressing these challenges. The role of technical and vocational education and training (TVET) in addressing these challenges has also been recognized. However, little is understood about the preparedness of TVET educators, specialists in their vocational areas, to address environmental and sustainability issues. This action research study examines the relationship between educator ecoliteracy and educator ability to develop and deliver vocationally specific sustainability education, in a single TVET college in Canada. The research study was undertaken over a 3-year period and involved 43 participants in total. Findings indicate that educators’ ecological knowledge, their collegial relationships and institutional processes are all significant in supporting attempts to revise vocational curriculum. Professional learning to improve vocational educators’ ecoliteracy requires more than deepening ecological knowledge, however. Also required is an understanding of the science of contemporary ecological issues and the relationship of those issues to broader socio-cultural and economic activities. In order to consider curriculum revision, educators thus need to personally value and prioritize such education as essential to the development of vocational competency amongst their students. Improving the ecoliteracy of the educator community, therefore, requires more than simply developing knowledge and skill in the scientific language of ecology; it also requires opportunities for educators to participate in institutional sustainability policies and practices
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".