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Record W4317543072 · doi:10.4324/9781003359920-6

Crossing Worlds: South–North Collaborations as Creative Encounters with Arts, Humanities and Sciences

2023· book-chapter· en· W4317543072 on OpenAlexaboutno aff
Kim Berman, Michelle LeBaron

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsVisual artsSociologyHumanitiesLiberal arts educationArtArt historyPolitical scienceHigher education

Abstract

fetched live from OpenAlex

The humanities and the arts are increasingly in crisis, yet have never been more needed. To illustrate challenges and possibilities in this crisis, we give examples of collaboration between north and south; and amongst scientists, artists, humanities and legal scholars. As a Canadian law professor and a South African visual arts scholar who co-founded a Johannesburg community art centre, we describe our collaborative work and its lessons. Overall, our work strengthened our convictions that while the humanities are in crisis, they are also in a state of becoming, hope and possibility. We describe two examples: HIV/AIDS prevention work in the early 2000s when artists and humanities resources helped stem the tide of infections in the midst of scientific misinformation about how the disease was spread. Our second example is a seminar abroad for Canadian law students in South Africa. As the responsible faculty members, we integrated arts and humanities pedagogies into the students’ experiences. Students found many encounters unsettling, and arts methods both ameliorated and, in some cases, accented, their discomfort. Discomfort, when normalised and engaged, gave way to curiosity and growth. This work proved useful in addressing broader questions of the role of arts and humanities in future democracies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.938
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.007
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.140
GPT teacher head0.301
Teacher spread0.161 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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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