Let Kids Sleep: The Role of Interdisciplinary Neuroscience Outreach in Stimulating Brains and Developing Research-Informed Approaches to Community Concerns
Bibliographic record
Abstract
Northwest Noggin (NW Noggin), an all-volunteer neuroscience education outreach non-profit, serves its community by bringing students, artists, scientists and other participants together for artistic collaboration and learning. The outreach takes place in K-12 schools and other institutions (such as museums, coffee shops and correctional facilities) all over the Pacific Northwest. Neuroscience education outreach generates discourse surrounding community concerns through illuminating the brain-centric qualities of issues and by drawing on neuroscience research to create solutions. The neuroscience research-informed perspectives on these concerns stimulate awareness, create momentum towards evidence-based reform, and can result in policy interventions. This thesis details how NW Noggin outreach helped address chronic sleep deprivation and its associated health risks for teenagers in the Vancouver Public School District. This was achieved when NW Noggin volunteers persistently re-sparked discussion surrounding the start times for high schools in the district while referencing neuroscientific evidence centered around research on sleep and developing brains. Further educational and communal concerns are analyzed in parallel with a demonstration of how NW Noggin works to explore research-informed initiatives and solutions. The efforts of NW Noggin are then positioned in the context of STEM education research by understanding how outreach efforts aim to improve metrics associated with student motivation and performance.
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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".