Mitigation and adaptation: Critical perspectives toward digital technologies in place-conscious environmental education
Bibliographic record
Abstract
This paper explores the tension for educators between the proliferation of mobile, digital technologies, and the widely held belief that environmental learning is best nurtured through place-based approaches that emphasize direct experience. We begin by offering a general critique of technology in culture and education, emphasizing what is at stake in the new era of digital tools and climate crisis. Building an analogy to the problem of climate change, the second part of the paper takes an “adaptation and mitigation” stance toward technology in environmental learning, and offers critical conceptual guidelines for policy and practice. Invoking language that describes the worldwide response to the climate crisis is a reminder of how the everyday devices we rely on are embedded in political, economic, and ecological webs of contention. Ultimately, we hope that describing some promising adaptations of these tools and their limitations will enable learners to better understand the relation between people, place, and planet, as well as the relation of people to their tools.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.093 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| 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".