Curriculum in Professional Practice: A spotlight on professionalism‐in‐context through dialogue
Bibliographic record
Abstract
This editorial introduces the second Curriculum in Professional Practice forum. This issue features our very first Curriculum dialogue, a unique format that allows for the development of dialogue between several authors, focusing on an area of curriculum-related research and practice. Such dialogue provides insight into the ways in which individuals value and negotiate different experiences, knowledge and perspectives around an area of practice. As part of this, we envisage that authors and readers will also be able to critically reflect on their own roles and contexts. The Curriculum dialogue format illuminates the professional expertise and judgement at play within a specific time and place. In so doing, this offers an antidote to the decontextualised and de-professionalised notions of teaching that are being constructed in some policy contexts (Hordern & Brooks, 2023; Mayer & Mills, 2021). At the same time, we see the Curriculum dialogue as a space in and of itself, whereby ‘professionalism-in-context’ (Kontovourki et al., 2018) emerges from the ‘bundle of trajectories’ (Massey, 2005, p. 54) that are brought together. In our inaugural editorial, we drew attention to the artificial binaries and boundaries that can exist when describing professionals involved in curriculum research and practice (Healy et al., 2024). In this issue, professionals are actively challenging binaries within subject curricula content and navigating constraints between ‘official, taught and experienced’ curricula (Priestley et al., 2021, p. 14). This includes exploration of the tensions between the prescribed curricula and the collaborative ways teachers might enact curricula that is shaped in-situ with their students. Contributors also draw upon experiences of working and studying across different country contexts, which appears to play a role in illuminating where there is potential to reconceptualise curricula content and reenvisage approaches to curriculum and teacher development. This issue comprises a Curriculum dialogue article and two Perspectives and reflections articles. The first paper, by Haira Gandolfi, Terra Glowach, Sharon Walker, Lee Walker and Elizabeth Rushton, builds upon Gandolfi and Rushton's (2023) Special Issue on Decolonial and anti-racist perspectives in teacher education curricula in England and Wales by opening space for a collaborative discussion between professionals working in teacher education and professional development across schools and universities. Gandolfi et al.'s dialogue allows insight into the forms of engagement teacher educators and school leaders have with decolonisation and anti-racism through their professional practice. The dialogue's concluding remarks address barriers and challenges, alongside hope for the ways that anti-racist and decolonial work can be taken forward. The second article, by Katherine Wallace, introduces an Indigenous-informed view of history education whereby ‘historical significance is reimagined as historical affect’. Wallace's studies in Vancouver, Canada transformed the perspectives on history education she initially developed as a history teacher in England. Within the article, this Indigenous-informed critique illuminates the binaries underpinning approaches to ‘historical thinking’ and addresses the consequences for history teachers in school classrooms when challenging these binaries. Finally, the third article, by Lottie Howard-Merrill, addresses the question of ‘What can curriculum contribute to preventing forced marriage?’ Howard-Merrill foregrounds her own professional expertise and that of the teachers involved in her research. In doing so, Howard-Merrill reveals challenges between prescriptive approaches to Relationships, Sexuality and Health Education (RSHE) and the richness of students' existing knowledge from an array of sources (e.g., documentaries, friends, social media and novels). The article raises questions about where there is capacity for teachers to cultivate space for young people to reflect on their own experiences, perspectives and lived realities. The article remains hopeful about possibilities for change whereby curriculum approaches could better create conditions for students' and teachers' agency in learning about gendered violence, including forced marriage. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".