An Investigation of Reliability Coefficients Estimated for Decision Studies in Generalizability Theory
Bibliographic record
Abstract
This research aimed to compare the G and Phi coefficients estimated in Decision studies in Generalizability theory and obtained in actual cases for the same conditions of similar facets by using crossed design. The research was conducted as pure research on 120 individuals (students), six items and 12 raters. An achievement test composed of six open ended questions and a holistic rubric developed by the researcher were used in data collection. Data analysis included obtaining the G and Phi coefficients by creating actual cases for two, four and six raters followed by D studies conducted for other actual cases with different measurement conditions to make estimates. Finally, G and Phi coefficients obtained and estimated for two, four and six raters were compared separately. Findings show that G and Phi coefficients estimated in D studies by increasing the number of raters were sometimes greater than those obtained in actual cases although they were sometimes smaller. However, it was concluded that a pattern may not always exist between the G and Phi coefficients obtained in actual cases for same number of raters and those estimated in D studies.
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 | 0.184 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".