Using art to understand emerging French as a Second Language teacher identities
Bibliographic record
Abstract
In this paper, we describe the ways in which our post-structural positioning towards language, learning and knowledge informed how we developed our arts-based research collection tools. The tools were first designed using a holistic and iterative process as means for French as a second language (FSL) teacher candidates in a Teacher Education preparation course to express themselves as plurilingual language educators and reflect on their practice. After the course was complete, students were invited to participate in three life-story interviews (Atkinson, 2007) to review, explore and explain their artistic creations and how these informed the personal and professional development as FSL teachers in becoming. In the following paper, we describe our conceptual framework, our ontological, epistemological and axiological positionings as arts based researchers, and how these have informed the tools we work with and how we work with participants in our research. After describing the tools, we provide concrete examples of the unique insights afforded into the participants' sense-making processes throughout the research project, such as accessing different types of knowledge, accessing different discourses that teachers engage with in the profession, identifying tensions and contradictions in teachers’ beliefs systems, and deconstructing and reconstructing teacher’s belief systems. Ultimately, arts-based research approaches allow for engaging with the complexity and nuance in teacher identity to promote identity/professional development, and for developing socio-emotional links to their sense of self throughout the research process.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".