Bibliographic record
Abstract
This chapter explores intersections of human and archival modes of memory in moments of archival repatriation. It recounts two repatriation experiences involving materials from the Indiana University Archives of Traditional Music (ATM)—one in which the author traveled to West Africa to repatriate media recorded in 1934 (around which this chapter is centered), and a second involving Assiniboine sisters from Saskatchewan rediscovering a lost song in the ATM’s listening library (with which the chapter concludes). Human and archival histories are mutually informative; as such, moments when people bring the two modes together also can become moments of new memory creation. The chapter argues that repatriation, understood as a meeting point of human and archival memory, can be deeply meaningful because archives are extensions of humanity. When the two modes of memory, archival and human, are brought into conversation, the result can be powerful, augmenting the potency and value of each.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".