Toward a Stranger and More Posthuman Social Studies
Bibliographic record
Abstract
Posthumanism has seen a surge across the humanities and offers a unique perspective, seeking to illuminate the role that more-than-human actors (e.g., affect, artifacts, objects, flora, fauna, other materials) play in the human experience. This book challenges the field of social studies education to think differently about the precarious status of the world (i.e., climate crisis, ongoing fights for racial equity, and Indigenous sovereignty). By cultivating a greater sense of attunement to the more-than-human, educators and scholars can foster more ethical ways of teaching, learning, researching, being, and becoming. In an effort to push the boundaries of what constitutes social studies, chapter authors engage with a wide range of disciplines and offer unique perspectives from various locations across the globe. This volume asks: How can thinking with posthumanism disrupt normative approaches to social studies education and research in ways that promote imaginativeness, speculation, and nonconformity? How can a posthumanist lens be used to interrogate neoliberal, systemic, and oppressive conditions that reproduce and perpetuate in-humanness? Book Features: A collection of essays that explore the phenomenon of posthuman approaches to social studies scholarship. Contributions by many prominent social studies education scholars representing seven countries—Canada, Norway, Russia, Spain, Sweden, the United Kingdom, and the United States. A foreword by Boni Wozolek and an afterword by Nathan Snaza, both of who have made significant contributions to critical posthumanism in education. Provocation chapters that push readers’ thinking about the various ways that posthumanism connects to teaching and learning social studies. Images of more-than-human entanglements (i.e., artwork, photography, poetry). Contributors include Asilia Franklin-Phipps, Muna Saleh, Sandra Schmidt, Mark Helmsing, Erin Adams, and Avner Segall.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".