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Record W4220923539 · doi:10.32920/ryerson.14660481.v2

Constructing the alcohol blackout; an (auto)ethnographic narrative collage.

2022· preprint· en· W4220923539 on OpenAlexafffund
Justyna Rechberger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsToronto Metropolitan UniversityYork University
FundersCentre for Addiction and Mental Health
KeywordsNarrativeAestheticsEthnographyReflexivityExperiential learningFeelingSociologyInterviewBlackoutAutoethnographyPhenomenology (philosophy)PsychologyEpistemologySocial psychologyLiteratureArtGender studiesPedagogySocial sciencePhilosophyPower (physics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.003
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.685
GPT teacher head0.664
Teacher spread0.021 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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