What We See as One River is a Convergence of Many: Three Convergence Commitments in University Teaching
Bibliographic record
Abstract
Michael Marker knew we are never just one thing. He often wrote about concepts that gesture toward convergence: To converge as a way of blurring boundaries; To converge as a challenging process of coming-together; to converge educationally in a murky, “alluvial” place of relationality that is only navigable through artistic and storied methodologies (Marker, 2017). Marker steadfastly resisted colonial structures that attempted to tidily delineate knowledge and compartmentalize the unknowable. In this article, I reflect upon Marker’s scholarship through the idea of convergence, and I outline three conceptual spaces of convergence that I have observed in his work. Through analysis of Marker’s body of work, and an attunement to his loving and poetic forms of resistance, I articulate my commitments in my role as a relatively new, non-Indigenous faculty member in his former department at the University of British Columbia. I think of convergence commitments as relational meeting places that can be at once joyful and also tension laden; they are necessary practices that help me to decentre and “muddify” Western ways of knowing that I have been socialized to enact in institutional spaces.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.089 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".