Preface: Institutional Adjacencies and Genealogies
Bibliographic record
Abstract
Institutional Adjacencies and GenealogiesI fell into Mass Capture because I wanted to know something about what it feels like to be racialized and severed from citizenship.These were research questions that came out of my previous work on race, affect theory, and Asian-Canadian literature and culture.Most of the Asian-Canadian literary work that I read dealt, in some way or another, with the grief of racial exclusion and exclusion from the sphere of citizenship.Just reading this literature, no matter how carefully, wasn't enough for the work I wanted to do.Especially not when I was confronted with an archive of grief and loss that was so enormous that it grabbed me and wouldn't let go.It began in 2009 when, as an experiment, I presented a paper on the relationship between emotion and citizenship based on a reading of the photographs of Chinese-Canadian head tax certificates known as "Chinese Immigration 5s, " or CI 5s.There was an archivist, Johanna Mizgala, in the audience.At the time, she worked at Library Archives Canada, an institution that I would end up collaborating, or conspiring, with -for the next decade.After my paper, she came up and asked me if I knew about CI 9s.Well, no.A few weeks later, I received an envelope containing a CD-R (remem-
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.114 | 0.021 |
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".