Inspiring the chefs of tomorrow with Ashley Marsh [Podcast]
Bibliographic record
Abstract
In this episode, James Golding speaks with chef lecturer and food-education advocate Ashley Marsh, a member of the Royal Academy of Culinary Arts, educator at the University of West London, and lead for the Chefs Adopt a School programme. His career spans professional kitchens, international events and extensive community outreach, all driven by a commitment to building young people's confidence and curiosity through food. Ashley shares how his passion began with family influences especially his grandmother and uncle and how cooking helped him gain confidence as a teenager. Early career experiences in Australia taught him discipline and the value of strong mentors. After time in business and industry catering, he moved into education, combining practical expertise with a mission to support the next generation of chefs. At the University of West London, Ashley focuses on practical skills, leadership, sustainability and seasonality. He and James discuss the importance of collaboration between hospitality and education to attract and retain new talent. Ashley outlines the Chefs Adopt a School programme, which teaches taste and sensory exploration, knife skills and cooking to help children understand food origins, build confidence, and try new ingredients. With many families lacking time or confidence to cook at home, school engagement becomes crucial and the results are immediate: children grow more curious and willing to taste new foods. The conversation highlights challenges young people face, from social media pressure to increasing anxiety around food. Ashley believes food education provides grounding, community and resilience. He shares memorable stories from working with young people and about his annual Christmas volunteering with Ronald McDonald House, where cooking for families reinforces the emotional impact of hospitality. James and Ashley compare international food cultures, reflecting on Italian festivals where teenagers confidently prepare traditional dishes. These experiences reinforce Ashley's belief that the UK needs stronger food engagement across homes, schools and communities. Ashley also uses pure maple syrup in teaching to discuss seasonality, provenance, natural sweetness and healthier alternatives to refined sugar showing how any ingredient can spark learning. The episode closes with a call for deeper partnerships between chefs, schools and the hospitality sector, and a more positive narrative about careers in food. Ashley believes hospitality offers creativity, opportunity and global pathways and that early, inspiring food education helps young people grow in confidence and find their place in the industry. Follow https://www.instagram.com/alice.fevronia/ and Maple from Canada UK www.instagram.com/maplecanadauk/ for more seasonal recipe inspiration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.085 | 0.026 |
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".