The nomadic wanderings of a bag-lady and her space chums : re-storying environmental education with feral figurations
Bibliographic record
Abstract
This thesis is a semi-autobiographical narrative, a serious fiction, in which I hold together the contradictions I have inherited and gathered (specifically in relation to the Western educational system) and learn to encounter the “Other” (real and imaginary others), including the other that is my constantly shifting/growing “self,” and attempt to find/foster nourishing alliances for transforming Environmental Education. I situated myself with new materialist theorists, specifically Donna Haraway, Rosi Braidotti, and Karen Barad, who attempt to think “through the co-constitutive materiality of human corporeality and nonhuman natures” (Alaimo & Hekman, 2008, p. 9) and provide useful tools (figurations, metaphors, and/or stories) for finding creative theoretical alternatives to the reductionist, representationalist and dualistic practices of the Western (Euro-American) metaphysics. Instead, shifting towards ecological, rhizomatic thinking and a nomadic subjectivity, I take up bag-lady storytelling, a performative new materialist methodology, a creative (re)twist of Ursula Le Guin’s (1989) “Carrier Bag Theory of Fiction,” a nomadic practice of wandering and gathering. Such wandering and gathering requires a different logic, an attunement and attentiveness to processes and practices of ongoingness (not simply endings). Sharing the stories and figures I gathered doing and thinking a performative Environmental Educational inquiry during a yearlong place-based eco-art project collaboratively undertaken with a Grade 4/5/6 class around the lost streams of Vancouver, I focus on the patterns created and the traces left by the multiple complex figurative entities encountered who wander throughout spacetime. I focus on the types of stories created and told from these traces, searching for stories of our shared vital vulnerability, stories that just might draw us together, gathering and holding all of our heteronymous ideas, beliefs, and theories. My goal was to draw attention to ways of being, ways of knowing, and ways of living (getting on) together, that disrupt, alter, revitalize, and might just lead to the practices of collective recuperation needed to sustain a vibrant lively future.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".