Bibliographic record
Abstract
Many people wear lip balm to moisturize their lips. In this research-informed action project I investigated some of the hidden issues associated with lip balms by examining some of the living and non-living components that are involved during the life cycle of lip balm. Each stage in the life cycle of a product (from extraction to disposal) has some negative impacts on the wellbeing of individuals, societies and environments (“Story of Stuff,” 2007). In addition, I conducted a study involving 40 teenage girls to learn more about their frequency of use of lip balms, the features of lip balms that appeals to girls the most, and what brands the girls preferred. My study reveals that 50% of the girls surveyed use lip balm on a daily basis. Fragrance appears to be the most preferred feature when selecting a lip balm. There are no specific colours or brands that stand out as most popular. Corporations use fragrance, colour and fancy packaging to attract consumers and, at times, distract them from some of the negative impacts of their product. I suggest alternatives to lip balms and tips to stay lip balm-free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.185 | 0.031 |
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".