Adoption + Adaptation In Performance Cycling
Bibliographic record
Abstract
This research has explored the relationship between cyclist and machine, through exploratory research methods and heuristic exploration by proposing a series of custom hardware interventions as way of enriching mountain bike rider’s experience. Changing the mountain bike rider’s experience and the mindset of the community of avid cyclists and industry by thinking outside the frame to thinking inside it. The work ahead focuses on performance hardware development as a gateway for avid mountain bike riders to think deeply about their relations with the bike, by exploring rider’s experience from a cyclist’s point of view, and by exploring ways in which industrial design can bring performance and improvement into the bicycle industry through adaptability of the frame to rider and terrain. Adaptability is a key factor between the terrain, the cyclist + machine. This research explores adaptable hardware systems allowing for changes or adaptations to the machine’s geometry depending on terrain, preferences or affinities. Focusing on the means of change, particular to the bicycles’ frame geometry, the frame transforms from the fixed hardware of a simple conveyance to a system of an enabler, making the cycling experience more emotional and self-reflective. Exploratory and applied research leveraging my own experience has emerged as a model, journaling and describing experiences, forming case studies for others to understand broadly how we meaningfully engage in bicycle riding. Cycling culture and its community as observed through volunteering for one of the most prestigious race in the world called the British Columbia Bike Race, served as multi method tool, where observation, conversations and stories helped inform my research, and explore how humans relate to objects and how adaptable objects become for a specific use. Applied heuristic assets and integrated experience design, with a focus on the user experience and its role in the cycling environment, demonstrates the very present cognitive improvement of human beings through the use of invention and mechanization. This research and the design outcomes will take into account several aspects specific to the bicycle like: Fit, Geometry, Materials + Processes, Culture + Ergonomics, accomplished through Practice based research by way of a collaboration and partnership with Landyachtz Bicycle Company, of Vancouver BC, where bicycles are hand built and crafted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".