Opportunities and challenges for social policy : engaging youth online.
Bibliographic record
Abstract
The benefits ofa free, globally available and rapidly expanding communication network waits for the next generation ofsocial policy practitioners who dare to challenge the traditional approaches to citizen engagement. Michael de Percy. Michael de Percy Michael de Percy lectures in politics at the University of Canberra and his research focuses on communications technologies and citizen engagement in Canada and Australia. In this article I outline some of the opportunities and challenges presented to social policy practitioners considering the use of social networking tools to engage with youth online. At present, most government uses of online social networking tools are IimitedlO placing advertisements on banners in applications such as Google's 'Blogger', Microsoft's 'My Space ' and the latest and most popular application, 'Facebook'. However, little research has been conducted
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".