Critical pedagogical transition in instructional content development: A vocabulary intervention using social media for Indigenous youth
Bibliographic record
Abstract
Indigenous students speak diverse languages, and many of them are English language learners (ELLs). Research has consistently shown that it takes at least 5–7 years for ELLs to catch up with their English-speaking peers in academic language skills. Inequitable access to learning resources, along with diverse political, socioeconomic, and historical issues, have led to Indigenous students’ persistent academic underachievement, in particular for Indigenous youth in high school, where considerably increased academic language demands in content areas place them at high risk of academic failure and lead to extremely high dropout rates. Technology may enable us to bypass costly infrastructure requirements to develop innovative language interventions, given Indigenous students’ increasing interest in digital technology use. Considering culturally responsive pedagogy, our project aims to develop a content-based literacy intervention using social media for Grade 9–10 Indigenous students. This includes 30 curriculum units we developed integrating the Indigenous tradition of oral storytelling as a springboard to engage students and support their learning of academic vocabulary. These units based on First Nations youth’s stories are aligned with Ontario curriculum in subject content areas. This paper focuses on content development of our instructional design, where we honor “Indigenous ways of knowing.” With a transformative approach to teaching and learning, the project designed to support Indigenous youth’s academic language development aims to go beyond academic success. The school has been proven to be a significant protective measure against adverse factors that lead to the high suicide rate among Indigenous youth. This intervention project capitalizes on technology to promote literacy engagement and overcome the impact of low socioeconomic status and the intergenerational effects of historical trauma that have negatively affected the well-being and academic progress of many Indigenous youth.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".