Research goes to the cinema: The veracity of videography <i>with, for</i> and <i>by</i> youth
Bibliographic record
Abstract
This paper addresses the use of participatory videography as a way of knowing and bearing witness to the complexity of young lives in educational research. We outline the principles for engaging young people in participatory videography. Working in the framework of humanities-infused praxis with, for, and by young people, we explore the place of visibility and invisibility. We identify what is gained, lost and unsettled in the use of video as a cultural process and production. We offer our theoretical and aesthetic considerations in relation to two projects. The first is a project about the youth mental health system in rural Canada, wherein we explore the fractured system visually through documentary filmmaking in the cinéma vérité cinema genre. The second is a project in which we are working with young Aboriginal Canadians who are framing the intersections of mental health and technology through filmmaking. We interrogate videography as a form of cultural production with the potential for engaging young people in educative experience, symbolic activity and cultural production. Youth videography offers opportunities for comparative education research in which social and cultural analyses are made visible. We explicate videography as a potentially meaningful experience for youth and for a deeper cultural analysis in educational research while addressing the tensions surrounding its claim to veracity.
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".