Language Matters: Developing Inclusive, Strengths-Based Practice in a Cluster of Resource Teachers: Learning and Behaviour
Bibliographic record
Abstract
Abstract Resource Teachers: Learning and Behaviour (RTLB) work with teachers to identify learning and behaviour needs of students who experience barriers to educational success. The language RTLB use can have a significant impact on teachers’ response to the inclusion of students with special learning needs and is key to improving educational outcomes for all learners. We examined the extent to which RTLB in New Zealand used inclusive, strengths-based language in initial meetings with teachers and whether shifts could be made through professional learning and development (PLD). Data collected included audio recordings, transcripts of initial meetings pre- and post-PLD, RTLB reflections on both transcripts, and questionnaires. Results indicate limited use of inclusive, strengths-based language prior to PLD. However, PLD that provided targeted opportunities to reflect on evidence of language behaviour and to practise requisite skills markedly increased RTLB awareness, knowledge, and skills with respect to inclusive, strengths-based language. Findings indicate that change often requires disrupting long-held beliefs and practices and a need for evidence of these to be able to do so. The findings have implications for the type of in-depth PLD needed to facilitate change in the language RTLB use when working with teachers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".