An R Package for Computing Canadian Assessment of Physical Literacy (CAPL) scores and interpretations from raw data
Bibliographic record
Abstract
The Canadian Assessment of Physical Literacy (CAPL) is the first comprehensive protocol designed to assess a child's level of physical literacy. Current approaches to analyzing CAPL-2 raw data are tedious, inefficient, and/or can lead to computation errors. In this paper we introduce the capl R package (open source), designed to compute and visualize CAPL-2 scores and interpretations from raw data. The capl package takes advantage of the R environment to provide users with a fast, efficient, and reliable approach to analyzing their CAPL-2 raw data and a "quiet" user experience, whereby "noisy" error messages are suppressed via validation. We begin by discussing several preparatory steps that are required prior to using the capl package. These steps include preparing, formatting, and importing CAPL-2 raw data. We then use demo data to show that computing the CAPL-2 scores and interpretations is as simple as executing one line of code. This one line of code uses the main function in the capl package (get_capl()) to compute 40 variables within a matter of seconds. Next, we showcase the helper functions that are called within the main function to compute individual variables and scores for each test element within the four domains as well as an overall physical literacy score. Finally, we show how to visualize CAPL-2 results using the ggplot2 R package.
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 | 0.008 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.049 |
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".