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Finding Elusive Resonances Across Cultures and Time

2021· book-chapter· en· W4246614048 on OpenAlexaff
Gerald C. Cupchik, Despina Stamatopoulou, Siying Duan

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsHistoryComputer science

Abstract

fetched live from OpenAlex

Abstract This chapter is about meaningful connection in media entertainment in relation to the concept of resonance during an era of social and technological acceleration. A hierarchical model is proposed with a desire for pleasure at the concrete foundation and an aesthetic appreciation of meaning at the more abstract and universal level. This range of experience is examined in the context of Greek and Chinese thinking about resonance. For Ancient Greeks, resonance describes interpretative and expressive events where concrete bodily and immersive practices shape experiences that may have ethical and sociopolitical effects. In Plato, it branched into (1) passive reception that mirrors a copy and offers no direct access to truth but might merely condition a person or (2) a communion that reveals the ideal. Aristotle stressed the dynamics of resonance in theater to build a relatively autonomous agent who appreciates and reflects on ways that causes affect human action in the social world. In the Chinese part of this chapter, we examine scholarship related to the concept of resonance during the Six Dynasties period (220–589 CE) as well as its intellectual roots from Confucianism and Daoism. Major issues explored include: the function of resonance in artistic creation and appreciation as well as its social function from a Confucian perspective; the method that helps people experience resonance with nature or cosmos from a Daoist perspective; and finally, the concept of vital energy across the cosmos which facilitates the more profound experience of resonance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.991
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.249
Teacher spread0.203 · 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
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
Published2021
Admission routes1
Has abstractyes

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