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Record W4392906898 · doi:10.32920/25417162

Where There is Lightness, There is Dark: Exploring Colourism in the Philippine Diaspora

2024· preprint· en· W4392906898 on OpenAlexaff
Alliya Lopez

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsToronto Metropolitan UniversityUniversity of Calgary
Fundersnot available
KeywordsThe artsDiasporaPresentation (obstetrics)Social mediaLightnessSociologyVisual artsArtPolitical scienceGender studiesMedicineComputer science

Abstract

fetched live from OpenAlex

This major research project highlights skin whitening in Filipino communities and its relations to critical race theory and colourism through a multi-methodological approach of content analysis and arts-based research. Researchers have examined how colourism in Asia and the Philippines have flourished, but little research exists regarding social media and its presentation of colourism through skincare companies. Further, there has been no incorporation of arts-based research in approaching Asia’s skin whitening practices. This project aims to explore the messages Filipino skincare brands promote to consumers on Instagram regarding skin tone while using the messages to guide a creative exploration. Twenty Instagram companies were analyzed for the initial stage of this project through a content analysis. The findings from the analysis resulted in the creation of artworks that respond to and create cultural messages about skin tone stratification in the Philippines.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.065
GPT teacher head0.249
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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