South Asian Canadian Digital Archive:: Fostering Knowledge Diversity and Equity Through Multilingual Knowledge Infrastructures
Bibliographic record
Abstract
Knowledge and its production is informed by the availability of and access to existing knowledge infrastructures. Historically, both the production of knowledge and knowledge infrastructures have been dominated and dictated by western1 schools of thoughts that elided, erased, neglected, and negated the existence of multiple epistemologies. This paper explores ways to understand the depth and breadth of global colonial legacies and epistemic coloniality—and locate the pathways out in the archives. Using South Asian Canadian Digital Archive (https://sacda.ca) as a case study, the paper questions ways to rethink, redefine, and refine the methodologies of traditional archives to enable spaces for open and inclusive scholarship. It further frames SACDA (1897 to present) as an open tool for building multilingual knowledge infrastructures and to bring the larger community into the process of collective knowledge mobilization, creation, and dissemination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".