Decolonized Listening in the Archive: A Study of How a Reconstruction of Archival Processes and Spaces can Contribute to Decolonizing Narratives and Listening
Bibliographic record
Abstract
In 2019, Stó:lō writer and scholar Dylan Robinson, and Tlingit curator and artist Candice Hopkins,created Soundings: An Exhibition in Five Parts, asking Indigenous artists and musicians to reflect onhow a score can be a tool for decolonization. In response, Indigenous artists contributed scores inthe form of beadwork, graphic notation, and more, effectively challenging traditional notions ofwestern colonial music-making and performance practices. Drawing upon the exhibit Soundings, aswell as Robinson’s book Hungry Listening: Resonant Theory for Indigenous Sound Studies (2020),this paper seeks to understand how to decolonize archives in ways that impact the description,preservation, and settler experience of music created by Indigenous artists. Robinson argues that byincreasing our awareness of and acknowledging our settler colonial listening habits, listeners canengage in decolonial listening practices that can deepen our understanding of how Indigenous songfunctions in history, medicine, and law. By centreing Indigenous Traditional Knowledge andstewardship in archival settings, Indigenous musical records can be described and preservedaccording to Indigenous frameworks. I propose the use of content management systems such asMukurtu and Local Contexts, as well as reparative archival description, to centre Indigenousframeworks and Traditional Knowledge in the archive. This paper also presents three case studies todemonstrate both the problematic aspects of current mainstream archival practices, as well as howMukurtu, Local Contexts, and reparative archival description can work to centre IndigenousTraditional Knowledge and stewardship.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.032 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".