MétaCan
Menu
Back to cohort
Record W4312523830 · doi:10.54916/rae.119190

Diversity Colour: Understanding Cultural Diversity

2019· article· en· W4312523830 on OpenAlexfundno aff
Chihiro Tetsuka, Maho Sato, Koichi Kasahara, Satoshi Ikeda

Bibliographic record

VenueResearch in Arts and Education · 2019
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceSocial Sciences and Humanities Research Council of Canada
KeywordsDiversity (politics)Cultural diversityGeographySociologyAnthropology

Abstract

fetched live from OpenAlex

The authors conducted a workshop intended to promote an understanding of cultural diversity.The participants engaged in a colour-arrangement activity at the InSEA European Congress 2018 Finland, held at Aalto University.During this workshop, we focused on colour as a device to show the participants' individual natures, including the socio-cultural background of each participant to support the creation of an understanding of cultural diversity.Art was made in the following way: the participants each selected three sheets of coloured paper and arranged them in relation to each other on a background piece of paper and then discussed the meaning of each colour on a worksheet that was passed out, with the participants naming the colour arrangements.An analysis of the data produced by the workshop showed that this activity was effective for promoting the understanding of self, others and other cultures and backgrounds.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.016
Scholarly communication0.0090.012
Open science0.0010.010
Research integrity0.0020.003
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 head0.303
GPT teacher head0.451
Teacher spread0.148 · 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 designNot applicable
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueResearch in Arts and EducationSame topicCategorization, perception, and languageFrench-language works237,207