If nothing changes, nothing changes: exploring doing classification differently
Bibliographic record
Abstract
Rationale: The aim of this research was to consider how to deliver athlete classification differently, including the use of remote technologies. Commonwealth Games Federation (CGF) utilizes a value-driven approach to sport, in part, through the integration of Para and mainstream sport. Data from 20 years of integrated Commonwealth Games (CG) reveal limited participation in Para sports from developing CW nations, and access to athlete classification was identified as a key hindrance.Methodological approach: Using a participatory approach (PARS), the research involved multiple stages; evaluation of classification capacity in the Americas and the Caribbean (A&C), Classifiers to develop sport-specific process models, and trials across five para-sports. Qualitative evaluation involved interviews, surveys, and focus groups.Findings: Analysis indicated strong support from Classifiers and athletes regarding increased access to classification, via remote technologies. Athletes/coaches indicated that access to classification through this project was a catalyst to ‘get started’ in their sport and informed training. Participants identified the value of saved time, travel, and funds, by accessing classification remotely.Future implications: Confirmation of classifications provided to participants is on-going as is robust knowledge translation. Discussions with sport governing bodies continue regarding the potential of remote classification to drive para/Para development and participation globally, initially at youth and non-elite sporting events.Research contributions: The imperative of both stable secure internet and on-site Medical Personnel, provides new and important sport management information. The checklist of mandatory elements for remote classification is original and provides a detailed framework for future scholarly work.
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 | 0.058 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".