Symposium: Critical Friendships, Poetic Relations: Weaving Inspirited Pathways Through the Academy
Bibliographic record
Abstract
In this symposium presentation, we explore how four women scholars are drawn to one another as a sustaining and life-giving practice in the hetero-patriarchal world of academia. Institutional attitudes, power, hierarchy and oppression (Betasamosake Simpson, 2014; Foucault, 2008; Kumashiro, 2002) hold both a visible and an invisible presence in the academy as individuals struggle with increasing workloads, institutional accountability and competition. We base our foundational understandings of these conditions in critical theory (Freire, 2000), and turn to ecology (Abram, 1996; Jardine, 2006; Macy & Brown, 2014) and Indigenous philosophies (Smith, 2012) for a response that is messy, heart-full, multivocal and rooted in good relations. We weave poetry and narrative writing to describe the well-worn pathways in the academy and to critically reflect on how we might step away from them to create fresh and renewed paths. Walking alongside each other in a courageous sisterhood, we see our criss-crossing footsteps, woven together like a braid, creating strength and new possibilities through relationships. We engage Indigenous and literary métissage (Donald, 2012; Hasebe-Ludt et al., 2009) as a form of life writing (Richardson & St. Pierre, 2005) and we will invite our audience into a dialogue that explores the deep influence of these relations on our well-being and success on our personal and academic journeys.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".