Coasts for Kids (C4K): a transdisciplinary science communication effort
Bibliographic record
Abstract
‘Coasts for Kids’ (coastsforkids.com) is a series of animations developed as part of a collaborative experience between children and their families, coastal scientists, teachers, community artists, coastal managers, and illustrators. The videos are targeted at Primary School kids and wider audiences. It was co-ordinated by scientists in the NW of England in partnership with Sefton Council and the Southport Eco Centre (UK). The scientific committee included coastal geomorphologists, physical geographers, coastal ecologists, and human geographers from Universities in the UK, Australia, Canada, Spain, France and Mexico. Educational & community support was an essential part of the project and included teachers, author and community artists, illustrators, and coastal managers. The episodes were narrated by school children aged 6-8 years old from the Merseyside area (Liverpool City Region, UK). The aim of the project was to empower kids (and adults) to understand some of the complex interactions regulating coastal dynamics at a variety of temporal and spatial scales, and to trigger awareness and interest on coasts from an early age. The episodes have reached hundreds of thousands in online media and have been watched in many countries including the UK, Spain, Australia, Canada, Portugal, Turkey, Ireland, Netherlands, Argentina, Mexico, Brazil, Germany, Colombia, South Africa, etc. The series was endorsed by the NW Regional Flood and Coastal Committee in England and became part of KS2 education packages (e.g., the Flooding Education Package at The Flood Hub and the Countryside Classroom). The language of the videos was adapted and carefully selected by our educational committee for its inclusivity, inviting diversity, and representativity, which is something particularly important in STEM disciplines. This talk will discuss the key elements of the success of C4K, including the steps undertaken by the transdisciplinary team (families, kids, scientists, and teachers) to develop the videos and make the final resource freely available to download and share. Important core elments in the project also included efforts to maximise kids' abilities to link ideas and develop their own understanding of coastal environments.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.021 |
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".