Bibliographic record
Abstract
Did you know Leeds Carnival is one of the oldest Caribbean-style carnivals in Europe? It started in 1967 and celebrates colourful costumes, music, and Caribbean culture—just like the big carnivals in Trinidad and Tobago, Notting Hill Carnival in London, and Caribana in Canada! Carnival costumes are full of sparkle, feathers, and bright colours. But many of the materials used—like plastic beads and glitter—can be harmful to the planet because they don’t break down easily. After the carnival, lots of costumes get thrown away, which creates waste. That’s why engineers and designers at the University of Leeds are working with local artists to find better ways to make costumes. They’re exploring how to use recycled and biodegradable materials, eco-friendly paints, and clever designs that can be reused or taken apart after the carnival. Now it’s your turn to be a carnival designer! In this activity, you’ll make your own crown using recycled and eco-friendly materials. You’ll learn how to be creative while helping the planet—just like the artists and engineers working behind the scenes of Leeds Carnival.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.587 | 0.319 |
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".