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Record W4311521011 · doi:10.18432/ari29670

Small Sounds in Familiar Places

2022· article· en· W4311521011 on OpenAlexaffvenue
Lauren Michelle Levesque

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsScholarshipIntentionalityMeaning (existential)Active listeningPoetrySociologyAestheticsPrincipal (computer security)Space (punctuation)PhotographyGenerative grammarPsychologyVisual artsEpistemologyArtLiteratureLinguisticsCommunicationPolitical sciencePhilosophyComputer scienceLaw

Abstract

fetched live from OpenAlex

In this article, the movement between the gravity and synchrony of love in pandemic times as revealed through the creative practices of poetry and cellphone photography is addressed. Informed by literature on slow scholarship published prior to and during the COVID-19 pandemic, the author explores the ways in which listening to and caring for the small sounds of a familiar place–here a rural backyard–can act as a generative theoretical space to reconsider the meaning of love and its implications for academic work. The principal questions include: how can the practice of writing poems and taking photographs foster the intentionality of slow time? How can immersing oneself in this time provide insight into perhaps worn-out conceptualizations of what is considered precious? What implications, if any, can these insights have for understandings of love and the need for slow scholarship post-pandemic?

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)0.002
metaresearch head score (Gemma)0.007
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: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.035
Scholarly communication0.0080.011
Open science0.0010.013
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.191
GPT teacher head0.441
Teacher spread0.250 · 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
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
Published2022
Admission routes2
Has abstractyes

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