Bibliographic record
Abstract
Place-based education has a long tradition in philosophy, and has been a cherished pedagogy for many in the field of environment studies. The practice of taking students outdoors to discover nature is first traced back to foundations in ancient Greece, in the writings of local chroniclers and in the teachings of Aristotle and the Stoics, and then into the Enlightenment and Progressive movement with Rousseau and Dewey. Contemporary bases are also surveyed in the pioneering work of Orr and Sobel, and the Heidegger-inspired writings of Ingold, Tuan, Relph and Bonnett. An instance of this immersive approach to teaching is then narrated, based on the author's repertoire in conducting place-based environmental education on the University of Toronto campus. Offering sketches of the different kinds of pedagogical interventions and resources used, it is hoped that this particular case will inspire readers to develop their own album of locally developed outdoor lessons. The ‘Philosopher's Walk’ depicted is a real place in Toronto (as in Kyoto), but it is also ‘imaginary’ in the sense of being a free-space for group or independent modes of critical and creative inquiry, adopting Goethe's and Hesse's concept of ‘pedagogical provinces’ for excursively playful self-discovery.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".