Recounting Huronia Faithfully: Attenuating Our Methodology to the “Fabulation” of <i>Truths</i> -Telling
Bibliographic record
Abstract
If telling the truth is considered vital to research methodology, what happens in methodological spaces where “telling the truth” is futile? In this article, we examine the limitations, possibility, and even desirability of normative forms of empirically verifiable truth-telling and the potentialities for storied or fabulated truths with regard to knowledges that have historically been dismissed by their audiences as unreliable and even deceptive. To do so, we draw from critical theory, and Deleuzian theory in particular, to offer a detailed theoretical framework for understanding the notion of fabulated truth. We then turn to our own research to describe a project that embraced the potential of fabulation as a deeply generative methodological practice in regard to better understanding experiences of trauma. This project, which involved working alongside people with intellectual disabilities who have survived institutional incarceration, used fluid arts-based methods to help engage the affective force of trauma to story multiple truths about an otherwise unspeakable history.
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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 | 0.094 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.062 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".