Science Fiction, Speculative Pedagogy, and Critical Hope: Counternarratives for/of the Future
Bibliographic record
Abstract
Abstract In times characterized by pervasive future narratives—technological utopianism, dystopian annihilation, neoliberal “progress”—and simultaneous, all-consuming eco-anxiety, how the future is addressed in schools is critically important as we navigate our complex relationship with the “Anthropocene” in education. In this chapter, we problematize restrictive curricular and pedagogical visions of possibility and, as an alternative, position science fiction and speculative storytelling as genres that offer pedagogical frameworks through which educators may center collective, speculative, complex narratives of the future that open up—instead of foreclose upon—possible paths forward and ways of engaging with the present. Grounded in genre studies, futures studies, and science and technology studies, as well as in speculative world building work conducted with secondary students, this chapter accordingly outlines the contours of speculative pedagogy. Speculative pedagogy is framed in this chapter as an approach that embraces and explores collective, open futures; centers interdisciplinarity as a central means through which we can come to envision complex future possibilities with students; prioritizes dismantling singular narratives of possibility and the future; and mobilizes science fictional and speculative storytelling modes to grapple with uncertainty and resist mastery as educators examine critical social, technological, scientific, and existential issues alongside their students.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".