Bibliographic record
Abstract
Education in/through the environment, or education with the environment (to use Gough’s (1987) phrase), can play a substantial role in raising environmental awareness, cultivating environmental sensitivity, challenging and re-ordering existing environmental values, and developing new ones. Gough (1989), for example, describes how the kind of experiences advocated by the Earth Education movement (van Matre, 1979, 1990; van Matre & Johnson, 1988; Cohen, 1990; Johnson, 2007) can be utilized in re-orienting students’ environmental understanding, taking them beyond the superficial to embrace the complexity, diversity, interconnectedness and dynamic nature of the natural environment. Some years ago, Woolnough and Allsop (1985) talked about the importance of students “getting a feel for phenomena” through handson experiences in the laboratory as a prerequisite for good conceptual understanding. The same kind of preparation may be essential to gaining the kind of conceptual understanding of the natural environment that leads to environmental literacy. So many of today’s children are strangers to the natural world, spending their time in a world of steel, glass and concrete. For them, nature exists as tiny isolated pockets — a small park, a window box, a tree-lined avenue here and there. Many are so protected and so urbanized that they have never felt the rough bark of a tree, experienced total darkness or silence, seen the full glory of the milky way, heard an owl’s call, watched a spider spin a web, or even paddled barefoot in a stream or pond. They haven’t walked in the forest, climbed a mountain, sailed down a river or explored a cave. They increasingly live in a virtual environment, with experience mediated by computerized devices that entail sensory deprivation of the natural kind and its substitution by the bleeps and burps of electronic gadgets. In consequence, their understanding of the natural environment is minimal and their attitudes towards it are ill-informed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".