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/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".