The potential of intersectional and reciprocal approaches in the education of newcomer students
Bibliographic record
Abstract
This study addresses the challenges faced by newcomer students in the host country’s education system. It emphasizes the importance of creating learning communities around these learners. To achieve this goal, we assisted various stakeholders including parents and educators in transcending language differences by examining each other’s educational system. Our approach aimed to cultivate positive identities within the context of transnational experiences, highlighting mobility as an asset in the host school system, challenging the prevailing deficit-oriented view of migration. Grounded in critical interculturalism and social justice principles, we studied STEM curricula from students’ home countries and created resources to encourage dialogue among school stakeholders. We proposed workshops for teachers and parents, emphasizing the recognition of intersectional identities and promoting reciprocal knowledge. Results show the effectiveness of intersectionality in understanding the complexities of individual trajectories while reciprocal knowledge revealed essential in fostering mutual understanding between families and teachers. Ultimately, the research seeks to revitalize literacy engagement for English Language Learners in STEM, promoting inclusivity and equity in education.
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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".