IMAGINING DIFFERENCE: TECHNOLOGICAL POSTHUMANIST METHODS FOR ARTS-BASED FUTURES LITERACIES RESEARCH
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".