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Record W6982372940

IMAGINING DIFFERENCE: TECHNOLOGICAL POSTHUMANIST METHODS FOR ARTS-BASED FUTURES LITERACIES RESEARCH

2023· article· en· W6982372940 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPraxisNarrativeAgency (philosophy)Futures contractPosthumanismFeelingTransformative learningQualitative research
DOInot available

Abstract

fetched live from OpenAlex

While the future does not exist, narratives of futurity have powerful sway upon the way things unfold in the present. Teachers’ implicit feelings and beliefs about futurity can impact student outcomes and their sense of agency to make a difference in the world. This paper describes an arts-based research project that seeks to both explore and cultivate creative ways of feeling, imagining, and writing futurity among a group of teacher candidates in a teaching writing course. The paper describes a futures literacies writing workshop along with an assemblage of methodologies that instrumentalize technological posthumanist theory towards imagining and storying future difference. This research positions and challenges the posthumanisms as an invitation to engage with the discrete centrality of human desire for preferable outcomes and to instead cultivate interest in the deeply entangled processes of knowing and becoming that constitute the (other-than) human. The project reaches imaginatively into the unknown, seeking not answers but creative possibility. By engaging with posthumanist and digital arts-based methodologies in teacher education and qualitative research it is hoped that new intra-agential narratives of both futures literacies praxis and futures-oriented qualitative research might emerge.

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)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.680
GPT teacher head0.729
Teacher spread0.050 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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