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Record W4200255158 · doi:10.29173/connections39

Building Bridges: A Conversation between Friends, about Language, Laziness, and Long-distance Running

2021· article· en· W4200255158 on OpenAlexvenueno aff
Matilda Tucker, Hannah Clarkson

Bibliographic record

VenueConnections A Journal of Language Media and Culture · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsConversationLazinessGossipStorytellingContext (archaeology)NarrativeDialogicEmpathyLinguisticsSociologyPsychologyCommunicationSocial psychologyHistoryPedagogy

Abstract

fetched live from OpenAlex

This conversation took place in a shared Google Doc over several occasions in April and early May 2021, between friends and colleagues, artists and writers, Hannah Clarkson and Matilda Tucker, in the context of an ongoing experiment in collaborative writing. In their individual and collective practices, Clarkson and Tucker explore potential embodiments in language(s) of thinking and dwelling in the ‘here and elsewhere’ of places and spaces they may not physically be in, across cultural, geographical and/or emotional distance. They are interested in how language can be employed as a tool for empathy beyond concrete linguistic understanding; how translation as method opens up to modalities of fictioning and collective storytelling; and writing as an experiment in sharing everyday struggles and building collective narratives of care. An attempt to bridge gaps between the here and elsewhere of Stockholm, Berlin and all the other places that in this time of pandemic we cannot be, the text below is not a conclusion but a conversation. It is a thinking out loud - or rather, on screen - together, on themes of language and translation; belonging and resisting; work and laziness; former and formless selves.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.555

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)0.0010.000

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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