Constructing the alcohol blackout; an (auto)ethnographic narrative collage.
Bibliographic record
Abstract
Alcohol-induced amnesia, or "blacking out," is a common, persisting, yet understudied phenomenon in today's “extreme” drinking culture, with potentially serious consequences. The scholarly literature on blackouts is especially limited in addressing the aftermath of the state, in which fragments of experience are "pieced together" and negotiated through collectively constructed narratives. In the present paper, I challenge the traditional blackout narrative by moving beyond the experiential “what happened?” to address the phenomenological, discursive and hermeneutical. What is it like? How do we feel about it? How do we talk about it? And why do we continue to drink ourselves past the point of recollection? Inspired by creative and (auto)ethnographic modes of life-writing and research (Richardson, Ellis, Denzin), I asked myself these questions by interviewing twenty-three others. Following the notion that story-telling and autobiographical remembering are fundamental to knowing and “re-writing” experience, especially “the life of feeling,” the project explores the role of such narratives in maintaining “problematic” drinking habits. In creating a self-reflexive discourse around non-remembrance and in assembling personal stories with existing research, art and theory in an accessible format, the project constructs and embodies the blackout experience, commenting on the ways in which it is written and talked about.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".