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Record W4378214348 · doi:10.32920/23159861

Am I Pretty? The Influence of Mainstream Media on Beauty Ideals

2023· preprint· en· W4378214348 on OpenAlexaff
Erin Nantais

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsBeautyMainstreamAestheticsPerspective (graphical)AdvertisingSociologyArtPolitical scienceVisual artsBusiness

Abstract

fetched live from OpenAlex

<p>Why is a specific, narrow, and stereotypical standard of beauty typically perceived as more valuable, more worthy, and more desirable than others? Commercial and mainstream media play a significant role in carefully crafting societal expectations of beauty standards and ideals. This research project analyzes well-known body-positive advertisements, campaigns, and popular mainstream media to identify trends and embedded messages that reinforce negative and stereotypical beauty ideals. This research takes a critical perspective on the ways in which commercial media can influence the way we view others and ourselves. This research suggests that unrealistic and narrow beauty ideals have been created and maintained through media with the ultimate goal of making a profit from consumers’ insecurities. The overarching contradictions and communicated messages of the analyzed advertisements and literature are reflected and communicated through text and photography in a digital magazine format entitled “Am I Pretty?”. This project utilizes messages and themes often communicated indirectly by commercial media and flips those messages back to the reader, encouraging deeper critical reflection about media messages of beauty we often passively internalize. “Am I Pretty?” is a critical reflection of beauty ideals embedded in mainstream media that we so blindly value and look up to. </p>

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.350
Teacher spread0.279 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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