Bibliographic record
Abstract
In this article, I seek to disrupt the idea of the meaningful engagement of young people in policy-making by raising questions about what it means to engage policy makers meaningfully in responding to the work of young people. Paradoxically, we have extensive work on how young people might become engaged in social research particularly through participatory visual methodologies and, increasingly work on how young people themselves voice their concerns about social issues through vlogs and other do-it-yourself social media platforms, and yet relatively little on how their productions can have an impact either directly or indirectly on the policy-making process. Participatory work with young people is often dismissed as being tokenistic or romanticized, and the term “from the ground up” policy-making runs the risk of being overused and undertheorized. To illuminate the issues, I draw on work with a group of policy makers responding to the photo images produced by young people, all students in Agricultural Technical Vocational Educational Training Colleges in Ethiopia. Critically their responses, ones like “the girl should just clean up the mess,” highlight the implications of audience and especially the notion of how adults/policy makers view young people in youth-focused projects.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".