Bibliographic record
Abstract
Abstract Critical approaches to English as a second language (ESL) education in Canada broadly fall under two intersecting orientations—inclusivity-focused and issue-focused. Inclusivity-focused education refers to critical approaches to ESL that valorize minoritized and/or Indigenous students’ voices, languages, and other semiotic resources in learning (in) English. This inclusive orientation aims to challenge systemic marginalization of multicultural voices and identities, destabilize static notions of languages and other modes of communication, and importantly, decolonize inequitable power structures inherent in academic and broader social setting. An issue-focused approach adopts an explicit critical agenda, using eco-social issues as the foci of curricular content to engage students in critical interrogation of social assumptions and participation in related class-based action research to simultaneously learn the language and enact change in broader communities. Recent trends in critical issue-focused inquiries also draw on posthumanist, socio-materialist, and Indigenous perspectives to offer more complex, interconnected, and distributed views of language learning and social change. These perspectives not only urge for alternative ways (cognitive, bodily, multi-sensory, affective, and spatial) of critical engagement but also a more human decentering perspective to understand the ethical interdependence of the human/non-human world.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".