The Development of Scoring Criteria for a New Picture Naming Task
Bibliographic record
Abstract
Objective: The purpose of the study was to develop a scoring system for a novel naming task suitable for assessing naming performance in younger (18-30 years) and older (65+ years) adults in monolingual English, monolingual French, and English-French bilingual groups. This novel naming task will serve as an important health service to help diagnose and assess cognitively impaired older individuals, while also serving as an educational tool for healthcare providers.Materials and Methods: The Naming Task consists of 120 images organized in the same randomized order, and are shown on a white background displayed on a computer screen using PowerPoint. Participants are instructed to name the image displayed. Monolinguals completed the test in their native language and bilinguals completed the test in English only, French only, and a bilingual administration. Scoring criteria was established based on the responses from testing.Results: Strict and lenient scoring criteria developed for the Naming Task are presented. Eight items were removed from the original Naming Task due to quality and/or clarity, inability to name the image, or too many alternate responses. Performance in monolingual English and French was similar in younger and older adults for strict and lenient scoring. Bilinguals performed better with bilingual administration and worse with French administration, where scores were the lowest of all age and language groups.Conclusion: The Naming Task appears to be suitable for monolingual French and English individuals. Results suggest that a bilingual administration should be used when testing English-French bilinguals.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".