SFL praxis in U.S. teacher education: a critical literature review
Bibliographic record
Abstract
This literature review analyzes the influences of systemic functional linguistics (SFL) in U.S. teacher education from 2000 to 2019. First, we describe how SFL has been contextualized in United States in response to changing demographics, new technologies, policies, and the impacts of globalization. Second, we outline our methodology, which yielded 136 publications from the fields of literacy research, teacher education, and applied linguistics. Third, we present four findings: (1) the main vehicles for introducing U.S. teachers to SFL theory and practice are grant-funded university-school partnerships, courses in colleges of education, and self-contained professional development workshops; (2) most interventions focused on introducing teachers to functional metalanguage and text analysis, with fewer focusing on multimodality; (3) SFL interventions positively influenced teachers’ level of semiotic awareness and ability to design focused disciplinary literacy instruction. Teachers’ critical awareness and confidence for literacy instruction were influenced to a lesser extent; and (4) more sustained investments in teacher professional development led to greater gains in teacher learning as well as a critical awareness of the relationship between disciplinary literacy practices and ideologies at work in K-12 schools. Based on these findings, we conclude with three recommendations for the future of critical SFL praxis in teacher education.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".