Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contents: Gunilla Holm/Harriet Zilliacus: Multicultural education and intercultural education: Is there a difference - Rauni Rasanen: Teachers' intercultural competence and education for global responsibility - Hannele Cantell/Matti Cantell: Global education in a multicultural school - Sonia Nieto: Solidarity, courage, and heart: What teacher educators can learn from a new generation of teachers - Mirja Talib/Sari Hosoya: Finnish and Japanese pre-service teachers' preparedness for diversity - Pirjo Lahdenpera: How to develop an intercultural school: Experiences from Sweden - Leida Talts/Airi Kukk/Maia Muldma: The language immersion program at school entry in Estonia - Kristin Adalsteinsdottir/Gudmundur Engilbertsson/Ragnheidur Gunnbjornsdottir: Multicultural teaching in Manitoba (Canada), Norway and Iceland - Arja Virta: Cultural diversity challenging history education: Why and how - Arto Kallioniemi/Kaarina Lyhykainen/Antti Rasanen: Headmasters' conceptions of school festival traditions - Leena Andonov: Interaction in consultation discussions: Building partnerships with culturally diverse families - Lihong Huang: Learning for work: Student career aspirations in China and Norway.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it