Working the Pedagogical Borderlands: An African Critical Pedagogue Teaching Within an ESL Context
Bibliographic record
Abstract
The global movement of people has increased the number of immigrant students in countries such as the United States and Canada. The number of immigrant teachers working with these students has also risen, especially those in English as a Second Language programs (ESL). Scholars have argued that the increase in immigrant teachers, and teachers of color more generally, along with pedagogies that draw from the personal and collective experiences of marginalized students is a needed corrective to the overly white, Eurocentric orientation to teaching and content found in schools. This article examines these assumptions by documenting the struggle of one Senegalese-American middle school ESL teacher who was attempting to utilize the culture of his students as a dominant part of his pedagogy. The findings indicate that this teacher found himself in a school context where his political desires for immigrant students were modified by institutional structures and disenabling discourses about immigrants. What he enacted was a borderland pedagogy that had elements of the culturally relevant pedagogy that he sought and the assimilationist pedagogy that was pervasive within the school. The implications suggest that placing responsibility on these immigrant teachers to challenge assimilationist and marginalizing tendencies in the schools may end up blaming these practitioners for influential factors that reside largely outside their control. Consequently, to meet the needs of those marginalized by society requires work with and on the possibilities and limits embedded in these borderland spaces of schools, teacher desires, and communities.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".