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Record W3024499531 · doi:10.33137/jaste.v5i1.34274

Modern Day Trojan Horse

2020· article· en· W3024499531 on OpenAlexvenueno aff
Zahra Sina, Nadia Abdullahi

Bibliographic record

VenueJournal for Activist Science and Technology Education · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingLotionTrojan horseAdvertisingPersonal hygienePsychologyReading (process)Product (mathematics)BusinessMarketingInternet privacyMedicinePolitical scienceLawComputer securityTraditional medicineComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.271
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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