Response to letter on ‘Which literacy for health promotion: health, food, nutrition or media?’
Bibliographic record
Abstract
Letter to ‘Which literacy for health promotion: health, food, nutrition or media?’ Paper: what about physical literacy and what can be learned from it? We appreciate the opportunity to respond to the July 2021 Letter to the Editor, penned Elsborg and colleagues, who read our article examining similarities and differences between four widely used ‘literacies’ related to health promotion—health, food, nutrition and media (Truman et al., 2020)—yet found it ‘too narrow’ since physical literacy (PL) was not also included in the analysis. Their Letter argues that ‘physical inactivity is one of the risk factors in developing non-communicable diseases’ and therefore, PL, ‘the literacy most closely related to physical activity’ should not be overlooked. We absolutely agree with the importance of both physical activity and PL. However, our article explicitly states that it emerges from the various literacies evoked when it comes to treatments of food marketing,...
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.032 | 0.030 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".