Bibliographic record
Abstract
<b>About the book</b>This picture book is for young children and adults alike. It can be read and explored together. The story is about three friends who went on an adventure down the river to build a playground. On their way they meet other animals who seem to put obstacles in their way. Read the story to discover what happens next. The story was written and illustrated by a group of individuals from different parts of the world who worked together online over 6 months. They also had the help of many other educators from around the world who generously contributed their ideas and the characters of the story also originate from their contributions. All committed to creating a socially just world where education is available to all. <br>Values such as sharing, collaborating and helping others are central to this mission of what is called open education and will help towards achieving the UN’s Global SustainableDevelopment Goal 4, “Quality Education for All”https://sdgs.un.org/goals/goal4<br>Some may recognise other stories and fairytales in this story. The illustrations are a remix of details of exhibits from the Rijksmuseum, Amsterdam, available under CC0 and new illustrations. <br>This project was supported by a Global OER Graduate Network (GOGN) Fellowship in 2020.<br><b>Useful Links:</b>https://zenodo.org/record/4703978#.YYGBN57P02w<br><b>The picture book team</b>Chrissi Nerantzi @chrissinerantzi, Principal Lecturer in Academic CPD, Manchester Metropolitan University, UK; open practitioner and researcher, picture book writer and crafter. She co-authored this story with Helene, Penny, Paola, Verena and Gino. She also co-illustrated the book with Ody. <br>Hélène Pulker @HelenePulker, Senior Lecturer in French, The Open University, UK, open practitioner, teacher trainer, language materials developer, researcher in open and distance education, Co-author of the picture book story.<br>Penny Bentley @penpln doctoral student at the University ofSouthern Queensland. She co-authored this story with the team. <br>Paola Corti @paola5373, Project Manager at METID, Politecnico di Milano (Italy) and OE Community Manager at SPARC Europe. She co-authored this story with the team.<br>Verena Roberts @verenanz, Adjunct Assistant Professor with the Werklund School of Education at the University of Calgary & Instructional Designer with Thompson Rivers University. She co-authored this book with the team.<br>Gino Fransman @ginofransman Academic Developer and#OpenEdInfluencers' Project Leader at Nelson Mandela University (South Africa), OE4BW project author/ mentor and advisory board member & Open Advocacy champion. He co-authored this story with the team.<br>Ody Frank @ody_frank, Sixth-Form animation and game design student. Ody co-illustrated and designed the book.<br>Bryan Mathers @bryanmmathers Founder, Visual Thinkery. Bryan provided mentorship to the illustrators.<br>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.515 | 0.397 |
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".