Seeing records: remediation in Canadian archival theory & practice
Bibliographic record
Abstract
This thesis explores the applicability of the media studies’ concept of hypermediacy in archival practices of reformatting – referred to here as remediation. Specifically, it provides a framework of practice which maintains the provenance of records, including the role of archival co-creators whose work impacts historical knowledge production in archives. The thesis grounds the practice of remediation in the history of archival practice in Canada, exploring the ingrained nature of this practice in Canadian archives and signals the need to re-conceptualize the practice in a way that maintains provenance acquired by records after they have arrived at an archive. The thesis analyzes two case studies of remediation through a framework of hypermediacy and demonstrates how thinking of remediation in terms of hypermediacy can inform a practice which maintains provenance and could additionally serve as a step in the right direction for institutional work on reconciliation.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.052 | 0.124 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".