Bibliographic record
Abstract
People desire to be good and, even more so, to be innocent of wrongdoing.Standing in the face of injustices across the world, we can seek a kind of redemption in trying to make the world better.And yet, as Antije Krog, Nosisi Mpolweni, and Kopano Ratele write, "Remember that one of the attributes of pain is that to have pain is to have certainty; to hear about pain is to have doubt." 1 First Nations researcher and teacher Susan B. Dion coins the term "perfect stranger" to describe the ways that, even with the best of intentions, teachers and students protect themselves from ethical vulnerability.In this paper, I will explore Dion's concept of the perfect stranger through the lens of Emmanuel Levinas' ethics.Juxtaposing two views of otherness (articulated briefly via Claudia Ruitenberg), I will first describe Dion's concept of the perfect stranger and then look to the philosopher Emmanuel Levinas to understand the ontological implications of such a position, remaining within the thought of these two thinkers in particular. 2 Two claims emerge from an ontological examination of the position of the perfect stranger.The first claim, that the perfect stranger is objective, will take up Levinas' discussion on objectivity and its relation to what he identifies as "a philosophy of power" in his early book, Totality and Infinity: An Essay on Exteriority (1958).3 Using Levinas' reversal on the philosophy of power, I will then bring Levinas and Dion into conversation regarding alternative approaches that break apart the position of the perfect stranger at a metaphysical level.From there I will extend beyond the metaphysical and examine
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".