Future teachers' relationships with physical and technological environments
Bibliographic record
Abstract
Is future teachers’ contact with the physical environment significant enough for them to choose to educate their students about sustainability? These digital natives stand out from previous generations by their way of living, working and learning. Does the use of ICT by these young adults contribute to distancing them from the physical environment? Are future teachers, better informed thanks to technology, committed to environmental action? This research based on grounded theory was aimed at understanding future teachers’ relationships with physical and technological environments. The analysis of individual and group interviews with Moncton and Montreal teacher education students reveals that future teachers maintain a sporadic relation to the natural environment. They are still conscious that nature provides them with calmness, rejuvenation, and beauty. The Internet, visited at least two hours daily, offers them distraction, social affiliation, and personalized information, as well as facilitates their tasks and contact with the World. Future teachers are critical and cautious in their use of ICT, but are not much involved in the environmental cause. This research emphasizes the need to work on future teachers’ relationship to the physical environment with outdoor activities to get to know, appreciate, analyze, and improve the natural and urban environments. Key words: education, environment, Social networks,pedagogy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".