MétaCan
Menu
Back to cohort
Record W4404623855 · doi:10.37119/ojs2024.v29i3.818

Punctuating Musical Diacritics of Water in Cross-species Context

2024· article· en· W4404623855 on OpenAlexaffvenue
Peter Cole

Bibliographic record

Venuein education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousNarrativeSilenceMusicalAestheticsConversationVisual artsHistorySociologyMedia studiesLiteratureCommunicationArtEcology

Abstract

fetched live from OpenAlex

There is an urgency for compelling new narratives of ecological survival that draw on Indigenous and ‘othered’ millennial intelligences and agencies. With a focus on the lifegivingness and sacredness of water, this paper is a call for collective inter-cultural cross-species oral-performative, recuperative conversations for re-learning to care for our damaged finite planet. Spirit-being is ever-present in the St'at'imc multiverse. Be humble, kind, respectful and at peace the elders tell us. Our original instructions teach us how to live together in harmony and compassion with the rest of creation. In this narrative score, musical signs, symbols spaces and terminologies gesture toward lyrical, rhythmic, somatic, sensate, collaborative performance, including pause-silence-beat. At a bend of the river, with ancestors and those to come, ubiquitous Indigenous tricksters Coyote and Raven speak on the page as dramatis personnae to encourage metamorphosizing from normalizing Euro-diacritics that extinguish and essentialize Indigenous oral expression. Joining the conversation are Sam Jim, a St'at'imc elder born in 1866, and German astrophysicists Helga and Viktor who are researching water beyond our shared earthly home, Viktor having had St'at'imc research experience in British Columbia. The text is meant to be read aloud. Keywords: indigeneity, narrativity, cross-species interdependency, sacredness of water

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.983

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.441
Teacher spread0.383 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes2
Has abstractyes

Explore more

Same venuein educationSame topicIndigenous Studies and EcologyFrench-language works237,207