Affect, Empowerment and the Complexities of Belonging: Cultivating a Humanistic Atmosphere in the ESL Classroom
Bibliographic record
Abstract
In this paper, I focus on the affective atmosphere of the Adult English as a Second Language (ESL) classroom. I argue that a humanistic learning approach can be a form of strategic resistance against the bureaucratization and standardization of publicly funded ESL programs for adult newcomers in Canada. Given the growing, top-down trend in our economically driven and technologically dependent society, there is a need to humanize the Canadian ESL classroom as a space for empathy and critical thinking. Through a literature review and semi-structured, in-depth interviews with former ESL learners and former ESL teachers, this paper reveals the psychological and political complexities of second language learning and cultural identity, as well as the pivotal role that an ESL teacher can play during the first few years of settlement. In this context, I also critique the racialized linguistic hierarchy embedded in Canada’s multiculturalism policy and exclusionary immigration and language policies.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".