Bibliographic record
Abstract
In a world drowning in plastic waste, a revolution is brewing. From January 23rd- 25th, South Africa played host to a groundbreaking meeting of international Plastics Pacts, signalling a unified global effort to tackle the plastic crisis. The Plastics Pacts, exemplifying public-private partnerships, have become a beacon of hope ahead of the United Nations Global Treaty to end plastic pollution. Major players, from fast moving consumer goods brands to governments, are joining forces, measured against science-based targets to combat the environmental havoc wreaked by plastics. Following David Attenborough's Blue Planet documentary, a global outcry spurred worldwide action. Companies, together with the Ellen MacArthur Foundation, formed the first Plastics Pact in the UK. The idea spread across countries, with South Africa emerging as the third country to embrace this transformative approach. Now boasting 14 plastic pacts, the network gathered for the first time in Cape Town. Representatives from Australia and New Zealand, Canada, Chile, Colombia, India, Kenya, Mexico, Poland, Portugal, South Africa, UK and the US converged to share ideas, successes, and collaborate on a mission to eliminate plastic pollution. Plastics Pacts have ushered in systemic changes, pushing for the elimination of single-use plastics and a boost in recycled materials in products. However, the challenge is far from over. “Eliminating single-use unnecessary plastics is something all the pacts have got as one of their targets, another target increasing the amount of recycled material in all your products…The plastic and mounds of plastic are growing all around the world. We're drowning in plastic. So, the good work of the Plastic Pact has to be scaled to the next level, has to spread to other countries and we really need more plastics treaties”, said Harriet Lamb, CEO of climate action NGO WRAP. South Africa, an early adopter of the pact, faces challenges. Despite significant strides, the nation generates a staggering 2.4m tons of plastic waste annually. The answer, according to Lamb, lies in reduction - a primary strategy to combat plastic pollution. (allafrica.com 1/2)
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.014 |
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".