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Record W4391436562 · doi:10.18432/ari29757

Moana (Pacific) Expressions of Design

2024· article· en· W4391436562 on OpenAlexvenueno aff
Sonya Withers, Charlotte Harper-Siolo, Samuel Hāmuera Dunstall, Pelerose Vaima’a, Kristina Gibbs, Alexander Te’o-Faumuina

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This article is a reflection on an attempt to create a space of flux through the concepts of positionality, vā and talanoa within the design academy. This was presented as an academic course, originally intended to address a gap in established learning, and to make space for intergenerational knowledge systems that were originally being shared outside of the studio (shared at the knee, through office hours, and in passing conversations). This sharing led to key questions regarding how we (re)craft our ways through our practices and what cultural conditions are needed to enable safe design and cultural production. Five students enrolled in the course and are featured as co-authors in this article. They whakapapa as Tangata whenua (Māori, people of the land) or Tagata o le Moana (specifically Sāmoan). They are enrolled in a range of design disciplines such as spatial design, fashion design, and concept design. Classes were held once a week over a 12- week semester period. These in-person classes involved reflecting and re-presenting our positional contexts, a sharing and setting of kai, hikoi to gallery exhibitions featuring Māori and Pacific art practitioners at an institutional level and a community level, alongside the sharing of scholarship developed on the concepts of vā and talanoa, while coming back to ourselves and our familial, generational social settings.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.244
GPT teacher head0.460
Teacher spread0.215 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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