Development of the “Performance Competence Evaluation Measure”
Bibliographic record
Abstract
The aim of this study was to develop a measurement tool, the “Performance Competence Evaluation Measure” (PCEM), for the evaluation of qualitative aspects of dance performance. The project had two phases. In the first phase a literature review was conducted to examine 1. the previous development of similar measurement tools, 2. descriptions of dance technique and dance performance applicable to the development of a qualitative measurement tool, and 3. theoretical models from somatic practices that evaluate and assess qualitative aspects of movement and dance activity. The second phase involved the development of a system for using PCEM, and testing its validity and reliability. Three judges from the professional dance community volunteered to test PCEM with a sample of 20 subjects from low-intermediate to advanced classes at a university dance program. The subjects learned a dance combination and were videotaped performing it on two separate occasions, eight weeks apart. The judges reviewed the videos in random order. Logical validity of PCEM was established through assessment by two faculty members of the university dance department and the three judges. Intra-rater and inter-rater reliability demonstrated correlation coefficients of 0.95 and 0.94, respectively. It was concluded that PCEM can serve as a useful measurement tool for future dance science research.
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 | 0.048 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".