Forced Migration: Ghosts of Familial Memory
Bibliographic record
Abstract
In Forced Migration: Ghosts of Familial Memory, I create a haunted archive through \nperformance and recorded conversations with my mother and grandfather. Our conversations \nfocus on my ancestors’ forced migration to Canada as children in the late Nineteenth and early \nTwentieth centuries. Beginning in 1618, the United Kingdom shipped as many as 150,000 \nchildren to the colonies; I create a haunted archive of sound recordings to tell stories of my \nancestors’ migrations – Joseph Hart, Louisa Hart, and Eleanor Copeland – while tracing the \nimpact of these stories through my own embodied and performative response. Performances are \nrecorded using video and still photography, and my mother and grandfather’s stories are \naccentuated through collected sound. This haunted archive contends with the official adoption \nrecords of my ancestors’ migration by highlighting the storytelling voices of my mother and \ngrandfather. My archive lives in a website which pairs performance documentation with audio \nrecordings. I use queer and hauntological theory to reflect on my experience as a haunted \narchivist in the creation of a sound and performative archive.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".