CACTI: Co-developing awareness of bi/multilingual classroom practices with a professional development survey instrument
Bibliographic record
Abstract
Abstract This paper introduces an instrument, the Classroom Approaches to CLIL and Translanguaging Inventory (CACTI), to help primary and secondary teachers and academic researchers collaboratively develop awareness of bi/multilingual practices when academic subjects are taught in English. While teachers can develop explicit awareness of their language policies and practices, and of critical gaps that require action toward more equitable and productive learning spaces, researchers can learn what different bi/multilingual practices mean to teachers, and what concerns are salient for teachers regarding these practices. The practices may include (1) teaching for knowledge transfer across languages, (2) cultivating linguistic analysis skills, (3) raising critical awareness of the value placed on different languages in society, (4) developing bi/multilingualism and bi/multiliteracies, and (5) involving ALL students’ languages in the social and academic life of the class, even if they are not the official medium of instruction. Besides explaining what brought us to develop the CACTI, we illustrate two ways it can be used for collaborative reflection, one with pre-service teachers and one with in-service teachers. For each application, we discuss how our understanding was challenged by teachers, and what insights we offer for teachers on theirs.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".