Becoming Method(ologist): A feminist posthuman autoethnography of the becoming of a posthuman methodology
Bibliographic record
Abstract
This paper contributes to the intersections of post qualitative methods, digital methods, and internet studies, by describing the becoming of a digital posthuman visual method. I use posthuman autoethnography to argue that in the production of this method, the “auto” or my academic selfhood is decentered and entangled amidst an assemblage of material, discursive, and affective forces such as neoliberalism, Trump era terror, and dataism. I introduce a multitude of data points typically not made to matter but through which these material, discursive and affective forces importantly flowed in this production of this method: emails, personal correspondences, restaurant conversations, self-reflection, conferences talks and responses to conference talks. I focus specifically on the moments where the values and principles of feminist posthumanism were jarred and destabilized or where I was made to choose between foregoing my values or redesign my method and myself as methodologist. I argue academics have a response-ability to show both the forces at play behind the becoming of qualitative methods and knowledge in academia.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".