Bibliographic record
Abstract
When I first considered the role of silence in forming alliances, the idea of writing about silence was intriguing and freeing. As I pondered silence, my body overtook my usual controlled, linear, and deliberate writing process. It had something to say about silence that my mind couldn’t contain. The more I focused on thinking, the more belligerent my body became. I squirmed in my chair and my eyes glazed over as I stared at the computer screen. No matter how forcefully my academic mind struggled to identify the perfect theory or compelling moment that I might neatly insert into a linear essay, corporeality hounded me, pushing me into silence. Only through an unexpected blurring of boundaries brought on by the disruption of the Cartesian mind/body split, and after hours of sitting in the silence of my discomfort, did I realize that longings for silence and bringing silence into words is messy, confusing, contradictory. John R. Barrie (2008) states that “When all mental ruminations are at last exhausted, genuine silence emerges” (p. 10). So, after days and weeks of struggling through thoughts, I immersed myself in silence and turned to my body for the knowledge I sought. I closed my eyes, breathed deeply, and turned my attention to my physical and emotional bodies to learn what they might show me about the silence that resides there. I catapulted out of the here and now into a conflated momentum where past, future, and present merged. Time shifted and I lost my “self” ... floating between yesterday and today, here and there, wholeness and fragmentation. Feeling drove me. Rage and joy surged, washing over me, unexpectedly driving energy to the keyboard. Thoughts emerged: I found myself anxious that the privileged whining of a 40-something, middle-class, white academic would consume the coexisting cellular reality of a trans1 identified, gender queer, fat
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".