Expert panel of Charlotte Mason scholars and practitioners, hosted by Sally Elton-Chalcraft
Bibliographic record
Abstract
Professor Sally Elton-Chalcraft, Director of the Learning Education and Development Research Centre, University of Cumbria, hosts this panel discussion involving various expert Charlotte Mason scholars and practitioners. Panel includes: Dr Deani Van Pelt, Associate Professor and Director of Teacher Education at Redeemer University in Hamilton, Ontario; Emeritus Professor Hilary Cooper, University of Cumbria; Professor Stephanie Spencer, University of Winchester; and Elaine Cooper, Heritage School, Cumbria. This panel of experts will reflect on our year of activities celebrating the life, work and legacy of Charlotte Mason. Throughout the year 2023 we have held online events and face to face talks at our Ambleside campus, University of Cumbria, providing delegates with knowledge and understanding of Charlotte Mason's pedagogy and influence. Our conference in July also provided opportunities for delegates to debate with specialists. The November expert panel session offers an opportunity to deepen understanding of Charlotte Mason's sphere of influence and delegates can expect to listen to, engage with, possibly challenge but certainly learn from our panellists and each other. In this penultimate panel session we can turn our focus to our own lives and consider what we will take from the Charlotte Mason Centenary activities, to inform future practice in whatever context we find ourselves.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.020 |
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".