Bibliographic record
Abstract
From the small scales of curbs, benches, and stairs, to the largest scale of buildings, and even edges of the city, skateboarders are continually imagining and inventing places of play. Even attune to the micro-scales of architecture such as texture, cracks, or the grain of materials - tactile experiences that become embodied by the skater. Architecture is a physical manifestation that positions bodies in space and time, and skateboarders are agents of spacial appropriation. Pushing the limits of play and form - challenging the intended use of objects in the urban environment. Skateboarding itself pushing the boundaries of who can participate. This project aims to investigate street skateboarders and their gaze in activating spaces. Looking at where they are skating, what are they skating, and what is the impact of how they use space at varying scales. Harnessing what I learnt from the skaters mindset - a lens in which they seek out opportunities for play. I want to rethink and reinvent the possibilities and opportunities for architecture and design to entice people to take advantage of play in public space as skaters do. The combination of the skater’s mindset which can be ignited in us all, and design, opening up a possibility for a new creation, to promote dynamic, playful behaviour to enrich public space.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.097 | 0.020 |
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".