Hauntings of Absence and Erasure: Black Archival Practices of Property Data
Bibliographic record
Abstract
Abstract This article analyses data at the intersection of digital geographies, critical data studies, and Black studies to bring clarity to relations, differences, and frictions between Black knowledge‐making and common data practices. I highlight artist Tonika Lewis Johnson's project, Inequity for Sale , and detail a genealogy of the data she uses in this project to illustrate how she situates these data within the afterlives of slavery. Drawing from Avery Gordon's theorisation of haunting and ideas towards absences and erasures in Black archival practice, I argue that absences in data can lead to narratives that focus on violence as a singular historical event that is isolated from a larger history of violence. I suggest that bringing a curiosity to these absences, rather than dismissing them or framing them as oversights, can help re‐situate data within a broader temporal‐relational context that brings a sense of Black humanity to the fore.
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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 | 0.093 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.028 | 0.073 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".