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Record W4283782005 · doi:10.20360/langandlit29620

Literacy Entanglements and Relationality, Time, Place, Space and Identity

2022· article· en· W4283782005 on OpenAlexaffvenueabout
Jing Jin, Lara Polak, Velvalee Georges, Yina Liu

Bibliographic record

VenueLanguage and Literacy · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversité LavalUniversité du Québec à MontréalUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsLiteracyIdentity (music)ScholarshipTheme (computing)SociologyConversationFace (sociological concept)Space (punctuation)Media studiesEpistemologyPedagogyLinguisticsAestheticsSocial scienceComputer sciencePolitical scienceCommunicationWorld Wide WebArtLaw

Abstract

fetched live from OpenAlex

Tawaw, or welcome, to this special digital edition of the Language and Literacy journal. This issue is a direct result of the scholarship shared by participants and the development and management of the 18th annual, but first digital, Language and Literacy Researchers of Canada (LLRC/ACCLL) pre-conference by the co-chairs[1] (Jing Jin, Lara Polak, Velvalee Georges, and Yina Liu). The theme, Literacy Entanglements & Relationality: Time, Space, Place, and Identity, aspires to engage researchers in moving beyond entanglement toward evolving relationality through and in the various dimensions of time, space, place, and identity. This theme was intended to create opportunities for researchers to attend to the complexities inherent in broadening and strengthening our understandings of language and literacy entanglement. We anticipated thoughtful conversation about how humans engage with literacy and language at various stages of relations, from superficial acknowledgment to exploring how our messages are transformed by identity, time, space, and place. We wondered how the course of literacy and language research might become more robust by attending to all dimensions, particularly as we move from face-to-face to virtual contexts and digital means.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.996

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.271
Teacher spread0.262 · 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 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
Published2022
Admission routes3
Has abstractyes

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