Growing Grass between Concrete: A Choose-Your-Own-Adventure Game for Rewilding Literature Pedagogies
Bibliographic record
Abstract
Abstract Wild pedagogies invites educators to engage with more-than-humans as co-teachers and co-researchers. In collaborating with city grass, this paper blends rhizomatic thinking, literary ecocriticism, and the rewilding of pedagogy within severely constrained circumstances. Citing cognitive, emotional, and physical benefits of engaging with free and flourishing nature, this research asks: How can the severe constraints of particular sociopolitical circumstances and disciplines, such as postsecondary literature courses, be creatively encountered to support engagement with flourishing more-than-human kin? It also asks: What would grass do? This paper walks readers through many barriers faced by city college humanities courses and suggests practical, creative work-arounds that, while focused on college literature classes, can be adapted to educators in diverse disciplines and contexts. Because we need playful thinking to think creatively — even on the brink of catastrophes — this paper is written as a choose-your-own adventure game. Educators will be invited to consider the institutional, geographic, academic, political, personal, and social barriers impacting their pedagogical choices. Ecologically concerned educators need pragmatic, creative, and compassionate support to envision how wild pedagogies pathways can be applied to their course loads. Here, these explorations are designed to be experiential and experimental, open-ended, and ultimately mutually liberating.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".