Empowering Non-licensed-in-English Language Teachers to Construct Professional Knowledge in Their Actual and Imagined Communities of Practice
Bibliographic record
Abstract
Research has accumulated important knowledge over recent decades on how licensed language teachers develop and learn from cognitive and socio-cultural stances. Yet, relatively little evidence exists on how non-licensed-in-English language teachers (NLELTs) grow professionally in their communities. Similarly, few studies have yet investigated the concept of “imagined communities” in the language teaching field with these particular types of population. This study attempts to fill this gap by exploring the possible forms of professional knowledge that NLELTs build through participation in the activities of a learning community. Four non-licensed language teachers participated in a nine-month collaborative-reflective process focused on language teaching practices, in a public school in Bogotá (Colombia). In analyzing their interactions and consequent products, we discuss three dimensions of knowledge construction propelled by the individual visions they brought into the community. Furthermore, we analyze how learning in that present community granted the teachers access to envisioned practices in imagined communities for a desirable future. Based on the findings we argue that success or failure in participation in present, real communities determines imagined affiliation to future communities, their practices and even the NLELTs’ preferred future positionings as professionals.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".