Body + Machine: Exploring Technological Fictions through a Collaborative Artistic Event in Schools
Bibliographic record
Abstract
Body + Machine, a collaborative artistic event, explores technologically mediated human relationships and their fictional portrayals. From a posthumanist theoretical stance, I examine this phenomenon from the implementation of three school projects with grade-schoolers, high school students, undergraduates, and artists in Quebec. I specifically ask: How does the mediatization of bodies through technology impact the means youth use to express their identities? In search of answers, I examine the concepts of posthumanism, corporeality, permutable identity, metaphoric fictions, and critical pedagogy. From the findings, I develop a posthuman pedagogy that: (a) gives access to technological fictions by critically discussing technology and presenting related artists' work; (b) varies youths' means of expression by combining informal and formal practices linked to technologies; and (c) articulates technological experiments with corporeal experiences in multidisciplinary, collaborative, and critical art projects. This pedagogy examines the ethical as well as the aesthetic aspects of technology in youth's lives.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".