Bibliographic record
Abstract
Personal hygiene products are used on a daily basis by many people. Many are comparable to the Trojan horse. On the outside, they appear to be harmless. They are contained in attractive bottles and they rely on misleading ads to attract consumers. However, these products may contain potentially harmful chemicals and many people are unaware of how individuals, societies and environments are affected in the various stages of the life cycle of many personal hygiene products. Our STSE issue deals with an everyday product that falls under the Trojan horse analogy–lotion. We are concerned that our peers and other young adults are purchasing lotions without the knowledge of how they came to stand on the shelves of a store. We conducted a correlation study between gender and popular lotion brands among teenagers and the reasons behind their choices. We came to the conclusion that more females than males were interested in popular lotion brands due to enticing features that targets mainly feminine interests (e.g. scent is an aspect of lotion that more females than males consider when purchasing the brand). For our actions, we prepared an educational mind-map on our issue and a video compilation where we interviewed female students on their reactions to various lotion brand commercials. Our actions are meant to inform the public about the controversies surrounding our issue and the techniques companies use to gain the attention of potential consumers.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.015 |
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".