“Luckily, he kept a journal” : Family as Collective Autobiography
Bibliographic record
Abstract
“‘Luckily, he kept a journal’: Family as Collective Autobiography,” is an autotheoretical piece in which I use gender theory, trauma theory, and archive theory to perform an analysis of my family, the relationships within it, and our kinship making habits in the wake of WWII. In 1940, my great-grandfather joined the Canadian army. Four years later, he was sent overseas with the 1st Battalion of the Royal Regiment of Canada. While travelling from Toronto to England, he started keeping a journal. In need of a confidant, he recorded moments of solidarity, friendship, and family building, but also profound fear and conflict. When he returned home, the weight of the war left him unable to share these memories out loud. Instead, he let his writing speak for him. The act of passing down written artifact became a tradition in my family. Rather than writing, my dad’s history is captured in drawings, which he passed down to me. Over time, I realized that the sketchbook—full of superheroes and cartoon characters—functions as a representation of our relationship. What the journal did for my dad, the sketchbook does for me. I argue that each time one of us reads the journal or looks at the sketchbook, we create a collaborative autobiographical account of our particular, yet individual lives. The fact that these artifacts make up an archive inspired me to interrogate the conventional definition of archive by writing the self as a kind of archive. These artifacts—my great-grandfather’s journal and my father’s sketchbook—are physical representations of the connections that make up my family’s collective autobiography, which resides in what I call my archive of self.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".