Beauty and the Packaging Beast: Plastic in the Beauty Industry
Bibliographic record
Abstract
The beauty industry's reliance on plastic packaging has negatively impacted the environment and human health.Although plastic packaging is convenient and cost-effective for cosmetic companies, it creates significant waste that tends to be overlooked.In this paper, we review the most common plastics used in the beauty industry by looking at their environmental and health implications, as well as alternative bioplastics.Our review begins with an overview of acrylonitrile butadiene styrene (ABS) and styrene-acrylonitrile (SAN), two commonly used thermoplastics for cosmetic packaging.However, both ABS and SAN are challenging to recycle and often end up in landfills.Polyethylene terephthalate (PET) and polypropylene (PP) are versatile plastics with good pliability but are challenging to recycle.High-density polyethylene (HDPE) and low-density polyethylene (LDPE) are commonly used for their flexibility and durability, with HDPE being easier to recycle than LDPE.Polyvinyl chloride (PVC) is flexible, durable, and compatible with a variety of chemicals; however, it poses environmental risks due to its high chlorine content.Some cosmetic companies are turning to biopolymers and bioplastics to counter some of the adverse effects of traditional plastics.These materials can be made from by-products of other industries, often require less energy to produce, and have fewer adverse environmental and health impacts.These alternative materials are necessary to create a more sustainable beauty industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".