Bibliographic record
Abstract
As I prepare this issue, over seven thousand children have died in the conflict between Israel and Hamas.Many thousands more have been injured, and entire generations on both sides of that border are being traumatised.It might seem that books for children are inconsequential alongside tragedies of this magnitude, but current international conservative movements to ban books suggest otherwise.The books read to and by children and young adults have the power to do far more than entertain, though they do that.Books can represent trauma suffered by young people and help readers understand suffering and regain hope.The first article in this issue, by Arya Priyadarshini and Suman Sigroha, is a study of children's books about the displaced populations of Palestine and Syria and the devastation they face.The authors trace the narrative strategies that balance the two extremes of suffering and optimism.Books can help young people see their place in a world that may threaten and victimise them.Later in the issue, Mazlum Dagdelen and Nico Carpentier analyse picturebooks about the Cyprus conflict, focusing on how depictions of childhood innocence and citizenship intersect with discourses of victimhood and war.Books can show children images of themselves in conflicts, as soldiers, and as damaged and traumatised, in need of healing.Zhang Shengzhen discusses that most classic of texts about a suffering child, Anne Frank's The Diary of a Young Girl, in a study of its introduction and reception in China.Ademola Adesola undertakes a reading of African YA novels that focus on the child soldier; he argues that paying attention to gender in these texts will ultimately be crucial for the creation of viable rehabilitation programmes.There may never be enough social programmes to address the violence that is currently being visited upon young people in Gaza and around the world, but I have to believe that the work we do, as scholars and practitioners working with children and their books, can and does make a difference.Part of that work centres on our ongoing critiques of social inequalities represented by books for children.In this issue, Ida Sachmadi, Aquarini Priyatna, and Lina Rahayu study an Indonesian young adult series that depicts poverty and shifting socioeconomic positions.As they trace the marginalisation suffered by lower-class characters in the series, the authors show how these texts encourage readers to question such social structures.Using qualitative analytic
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.081 | 0.053 |
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".