Tracing the Landscape: Re-Enchantment, Play, and Spirituality in Parkour
Bibliographic record
Abstract
Parkour, along with “free-running”, is a relatively new but increasingly ubiquitous sport with possibilities for new configurations of ecology and spirituality in global urban contexts. Parkour differs significantly from traditional sports in its use of existing urban topography including walls, fences, and rooftops as an obstacle course/playground to be creatively navigated. Both parkour and “free-running”, in their haptic, intuitive exploration of the environment retrieve an enchanted notion of place with analogues in the religious language of pilgrimage. The parkour practitioner or traceur/traceuse exemplifies what Michael Atkinson terms “human reclamation”—a reclaiming of the body in space, and of the urban environment itself—which can be seen as a form of playful, creative spirituality based on “aligning the mind, body, and spirit within the environmental spaces at hand”. This study will subsequently examine parkour at the intersection of spirituality, phenomenology, and ecology in three ways: (1) As a returning of sport to a more “enchanted” ecological consciousness through poeisis and touch; (2) a recovery of the lost “play-element” in sport (Huizinga); and (3) a recovery of the human body attuned to our evolutionary past.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".