Leading Inclusive Education: The Impact of a City’s Place-Based Pedagogical Partnership with Higher Education on Secondary School Inclusion
Bibliographic record
Abstract
The focus of this article is leadership for inclusive education. The work presented explores a three-year (2021–2024) place-based pedagogical partnership (PBPP) with schools and higher education (HE) leading change for inclusion and school improvement across Plymouth city’s secondary school network. National and international governments emphasise the role HE has in partnering with its local communities to achieve equal education access, inclusive pupil progress and student equality. The aims of this project and partnership were firstly to build meaningful relationships across secondary school leadership teams, local council, regional government and two city universities, and secondly to develop sustained practices that had evidenced impact on inclusion. The data reported highlights how the project co-created new practices, communication channels and policies, which contributed to school improvement, inclusive education and the development of new pedagogy. A pedagogical partnership model was established, taking a critical collaborative approach in aims and working from a place-based, contextual space. Findings show how the partnership impacted on the collaboration style of school leaders; improved pupil attendance, attainment and achievement developed new Continuing Professional Development (CPD) programmes; developed alternative education provision; and established a mentoring programme for school pupils. Outcomes and findings contribute knowledge to the evolving discourse of inclusive education, emphasising the relevance and impact of place-based pedagogic partnerships working across institutions to lead inclusion.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.033 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".