From the sidelines to the headlines: How youth leveraged online technologies during the pandemic to drive a national policy advocacy campaign against predatory diet pills — A case study
Bibliographic record
Abstract
Soon after the onset of the COVID-19 pandemic in 2020, much of the analog world as we knew it ground to a halt, jettisoning most arenas of life into the digital sphere. Among those who quickly gained facility with online ways of interacting and conducting business included youth and U.S. state lawmakers — two communities that rarely interacted prior to the pandemic. Within months, key means of civic participation had shifted online, leading to the genesis of the Youth Corps, a virtual youth policy advocacy program of the Strategic Training Initiative for the Prevention of Eating Disorders (STRIPED). In this case study, we introduce the theoretical frameworks that provide the basis for the digital STRIPED Youth Corps, describe the growth and successes of the program through the early years of the pandemic, and discuss lessons learned and future directions for continued virtual youth policy advocacy.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".