Bibliographic record
Abstract
In this chapter, Aitken and Rowlett introduce The Film Landscapes of Global Youth: Imagining Young Lives . They outline that, in the chapters that follow, there are stories and imaginings from Iran, Finland, Ireland, Sudan, England, Indonesia, Chile, Tanzania, The Faroe Islands, France, Argentina, Italy, Canada, and Cambodia. The stories are all about children and young people, some contextualized in the contemporary urban world or rural development, and others contextualized in ancient Celtic landscapes or science-fiction scenarios and other worlds. Some of the young people are contained by their landscapes, while others embark on journeys or migrations. As readers, we encounter street children, Roma families in non-Roma contexts, a child with Down syndrome who is forced on a precarious journey, Indigenous children facing change in the ways they are represented, monks and novices battling invaders, warriors battling dragons, young people living beside uncertain geopolitical borders, and rural children documenting economic restructuring. We encounter the ways young people relate to nature and the non-human, and how they use technology to become more-than-human. Aitken and Rowlett explain how the volume is an examination of what is possible for the future of research within the intersections of geography, film theory, and children’s studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".