Bibliographic record
Abstract
<p>The problem with many archives is that they are searchable only by supplementary metadata (anecdotal data not provided by the original source), rather than secondary metadata (descriptive information that covers dates, origin, history, and cross-referencing); information about a visual object is not always reliable, especially when it comes to Black Canadians. Supplementary metadata in Canadian archives are not classified by race or ethnicity, thus, the very structure of the archive erases from public memory the lived experiences of Black Canadians. Given the move toward digitization over the last fifteen years, the importance of the archive has become a topic of discussion. Since the public can now search through on-line collections, the need to protect and promote material archives has never been more important. This paper will explore the question of the archive-as-subject, rather than archive-as-source, through storytelling. Storytelling is one of the many cultural expressions that have connected Black populations. Using first-person narrative, I give examples from my ten-year-long experience working in Black Canadian archives to probe how the archive can move from its depository role to become a site where memories about Black Canadian experiences across time, space, and place are curated and narrated. What are the ethical challenges around this kind of reform?</p>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".