Archives and artificial intelligence: The Métis Oral History Transcription Project by the Esplanade Archives
Bibliographic record
Abstract
This paper describes how the City of Medicine Hat Archives sought to determine the validity of transcribing its collection of well over 1,000 oral recordings, in order to enhance their accessibility. This endeavour — the Métis Oral History Transcription Project — was conducted during the summer of 2023, using Descript (a subscription-based artificial intelligence audio transcription application) to transcribe a selection of significant oral histories, cared for and made accessible through the Esplanade Arts and Heritage Centre. As an act towards Truth and Reconciliation and to amplify voices historically stifled by the ‘official record’ that dominates so many archives, nine Métis oral histories were selected to be made more accessible to the public. The software proved extremely useful, but the transcription of oral recordings remains a labour-intensive undertaking. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".