From the periphery to the centre: Securing the place at the heart of the TESOL field for First Nations learners of English as an Additional Language/Dialect
Bibliographic record
Abstract
Indigenous learners of English as an Additional Language or Dialect (EAL/D) have historically not been the central focus of TESOL expertise here in Australia, or overseas. Despite moves towards inclusion increasing over the last two decades, there is an ongoing tendency for Indigenous EAL/D learners to remain on the periphery of current TESOL advocacy, research and practices in Australia. They are still often overlooked, as identification processes and support settings for migrant and refugee services are mismatched to Indigenous EAL/D learning contexts. Indigenous EAL/D learners, especially with un-/under-recognised contact languages (creoles and related varieties), can remain invisible in classrooms with mainstream curriculum and assessment practices (Angelo, 2013; Angelo & Hudson, 2018; Gawne et al., 2016; Macqueen et al., 2019). Hence, we argue that understanding and consideration of Indigenous EAL/D learners’ needs should become a priority in TESOL initiatives. This paper aims to place Indigenous EAL/D learners at the centre by alerting the TESOL field to a recent body of research and development on new Indigenous contact languages and whole class EAL/D teaching and assessment practices. Clarifying substantial issues and providing solutions, the paper makes Indigenous EAL/D its central focus, highlighting areas that otherwise result in “forgettings” about needs particular to Indigenous EAL/D learners.Thus informed, the Australian TESOL profession will surely include First Nations EAL/D learners at the heart of future discourse and initiatives.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".