Identity Texts and Academic Achievement: Connecting the Dots in Multilingual School Contexts
Bibliographic record
Abstract
The construct of identity text conjoins notions of identity affirmation and literacy engagement as equally relevant to addressing causes of underachievement among low socioeconomic status, multilingual, and marginalized group students. Despite extensive empirical evidence supporting the impact on academic achievement of both identity affirmation and literacy engagement, these variables have been largely ignored in educational policies and instructional practices. The authors propose a framework for identifying major causes of underachievement among these three overlapping groups and for implementing evidence‐based instructional responses. The framework argues that schools can respond to the devaluation of identity experienced by many students and communities by exploring instructional policies and strategies that enable students to use their emerging academic language and multilingual repertoires for powerful identity‐affirming purposes. Drawing on projects involving First Nations and immigrant‐background multilingual students, the authors document the profound transformations in academic, intellectual, and personal identity that multimodal identity text work is capable of engendering.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".