Planning and analysis of measurement reliability studies
Bibliographic record
Abstract
In the traditional plan for assessing the reliability of a measurement system, a number of raters each measure the same group of subjects. If the system has a large number of raters, we recommend a new set of plans that has two advantages over the traditional plan. First, the proposed plans provide greater precision for estimating the intraclass correlation coefficient with the same total number of measurements. Second, the plans are flexible and can be adapted to constraints on the number of times any subject can be assessed or the number of times any rater can make an assessment. We provide a simple tool for planning a reliability study, access to the software for the planning in the case where there are constraints and an example to demonstrate the analysis of data from the proposed plans. The Canadian Journal of Statistics 39: 344–355; 2011 © 2011 Statistical Society of Canada Dans un plan traditionnel pour déterminer la fiabilité d'un système de mesures, plusieurs évaluateurs mesurent tous les sujets d'un même groupe. Lorsqu'il y a un grand nombre d'évaluateurs, nous recommandons un nouvel ensemble de plans qui possède deux avantages par rapport au plan traditionnel. Premièrement, les plans proposés procurent une plus grande précision pour l'estimation du coefficient de corrélation intraclasse avec un même nombre de mesures. Deuxièmement, ces plans sont flexibles et ils peuvent être modifiés pour contraindre le nombre d'évaluations par sujet ou encore le nombre de mesures faites par un évaluateur. Nous suggérons un outil facile d'utilisation pour planifier une étude de fiabilité et pour utiliser le logiciel de planification lorsqu'il y a des contraintes. Nous présentons aussi un exemple pour illustrer l'analyse de données partir des plans proposés. La revue canadienne de statistique 39: 344–355; 2011 © 2011 Société statistique du Canada
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".