Learning by walking: non-formal education as curatorial practice and intervention in public space
Bibliographic record
Abstract
This case study focuses on Walking Home Carrall Street, a series of walks with youth that took place in the autumn of 2010 on and around Carrall Street in Vancouver, BC. Through participant observations, interviews and analysis of the written reviews submitted by the youth, the purpose of the study is not to provide generalisable insights, but rather to discern with which category or categories of educational programmes it may share certain features. The central question guiding the study, therefore, was: How might Walking Home Carrall Street best be characterised as an educational programme? By drawing out connections to educational, philosophical and geographical literature, I discuss obvious features explicitly mentioned by the programme’s organisers, such as its nonformal and experiential character, as well as less obvious ones, such as the ways in which the programme constitutes an intervention in public space and the ways in which it offers youth opportunities to manifest their intelligence. I also discuss curricular features, such as the deliberate use rather than avoidance of repetition and the relevance of emergent and unplanned curriculum.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".