A Suspicion of Testimony and the Tradition of Institutional Contexts: An Exploration of Translative Practice for Conducting Ethically Responsible Research
Bibliographic record
Abstract
If we consider the claim made by Dr. Theodore George at the 15th annual Canadian Hermeneutic Institute Conference, that there are unique contexts that require an untethering of ourselves from our tradition to better put ourselves into question, or what he considers to be an interpretative practice of translation that allows us to be “abroad in the world,” then I argue the tradition of institutional contexts is one such context requiring this necessary task of translation. I argue the testimony of the marginalized who practice within institutional contexts is tangled within institutional culture, practices, and structural power imbalances, requiring a practice of translation on the part of the hermeneutic researcher, in complement with that of conversation. This paper will explore the ways in which my doctoral research of healthcare aides’ experiences with death and dying in institutional contexts during the COVID-19 pandemic requires me to be both open and suspicious of my topic so that I may put the tradition of institutions into question to better consider the ethical obligations of my research. I will also make the claim that George’s idea of translation in hermeneutic research may lend itself to that of suspicion, but in ways that avoid some of the crevices of criticality that would serve to distance the reader from text or support explanation instead of understanding.
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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 | 0.263 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.046 | 0.215 |
| Scholarly communication | 0.041 | 0.042 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".