Public Health Managers' Perspectives on the Use of Social Marketing Among Public Health Nurses in Saskatchewan
Why this work is in the frame
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Bibliographic record
Abstract
We present a new method for investigating children's language comprehension and argue that it has the potential to mitigate known task-related biases and expose children's grammatical and lexical knowledge in a more natural and ecologically valid manner. The new method consists of filling in a digital coloring page, according to sentence stimuli (e.g., The green monkey is being scratched by the blue monkey; The rabbit is red.). Through the playful act of coloring in the page, children reveal their interpretations of grammatical constructions and lexical items. We argue that this method gives more accurate results than existing methods, in which children are asked to choose among several pictures representing a number of possible interpretations. We present two experimental studies: one with Dutch-speaking children, tested on four types of grammatical constructions, and a second study with children learning Dutch as a second language, tested on their vocabulary knowledge. In both studies, the new method was compared with one of the most widely used methods: the picture selection task. In the first study where children's performance is said to be underestimated, the new method reveals better performance whereas in the second study where children's performance is assumed to be overestimated, the new method reveals lower performance. The results suggest therefore that the new task indeed decreases external task-related effects and offers a more reliable measurement of children's linguistic knowledge.
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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 | 0.094 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it