Watching Bald Eagles Change Shifts: Seeking Digital Curriculum Access Across Canada
Bibliographic record
Abstract
There is an inconsistency between a growing need for national research on issues of child and adolescent health and the wide range of diverse curriculum responses to health issues undertaken by individual provinces and territories in Canada. Measuring the effect of interventions is more difficult in this contradiction. In this study, the authors uncover a growing need for national research, knowledge mobilization, and the development of a common language and Internet protocols to enable sharing of health education initiatives using the affordances of technology. The authors find that in an era where difficult social challenges for children and adolescents require not only national but global attention, the current jurisdictional structures present significant and challenging barriers to accessing national and global expertise. These barriers will need to be addressed in order to maximize the affordances of digital technologies for knowledge mobilization toward the goal of coherent pan-Canadian health curriculum approaches.Il existe une incohérence entre le besoin grandissant pour de la recherche nationale relative à la santé des enfants et des adolescents d’une part et la diversité dans la gamme de programmes d’études portant sur des questions relatives à la santé que proposent les provinces et les territoires au Canada. Ce manque de continuité rend plus difficile l’évaluation de l’effet des interventions. Dans cette étude, les auteurs révèlent un besoin grandissant pour la recherche nationale, la mobilisation des connaissances et le développement d’une langue commune et des protocoles Internet pour permettre le partage d’initiatives en éducation à la santé en profitant des capacités de la technologie. Les auteurs ont trouvé qu’à cette époque où les défis sociaux de taille auxquels font face les enfants et les adolescents nécessitent une attention non seulement nationale mais mondiale, les structures juridictionnelles actuelles posent d’importantes barrières redoutables à l’accès à l’expertise nationale et globale. Il faudra surmonter ces barrières afin de maximiser les capacités des technologies numériques en matière de mobilisation des connaissances pour arriver à des approches aux programmes d’éducation à la santé qui sont cohérents de par le Canada.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".