Bibliographic record
Abstract
21st Century learners live in a shrinking world with advances in technology and transportation with political, social and economic choices made in one corner of the globe affecting the opposite (Friedman, 2005; Zahabioun, Yousefy, Yarmohammadian, & Keshtiaray, 2013). To help navigate this changing landscape, global citizenship is an important life-skill for youths. UNICEF (2003) describes lifeskills in three dimensions: cognitive, personal, and interpersonal. These can be enhanced through the provision of high-quality international literature in the school library. Critical reading of translated literature provides an opportunity for youths to enhance their life-skills in reading the world and connecting their own experiences to others (Buck et al., 2011; Louie & Louie, 1999). Using the UN’s Declaration of the Rights of a Child (1959) as an analytical lens, we identify powerful examples of youths enacting agency and managing profound difficulties related to their cultural memberships in a set of award-winning translated titles.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
School librarianship paper on using international literature to build global citizenship in pupils; concerns school library pedagogy, not researchers' information behaviour.
The paper analyzes translated literature and youth agency in educational contexts.
School-library/global citizenship education via translated youth literature, not researcher information behaviour.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".