Reconstructing Alberta Working-Class History via Oral History: A Challenge for a Politically Engaged Academic
Bibliographic record
Abstract
Until recently, the literature on Alberta working-class history focused on institutions and on major events, particularly strikes and protests.1 But what do the participants themselves make of their lives as workers and in some cases militant workers and trade union activists? What do they regard as their victories and their failures? Interviews by the Alberta Labour History Institute have focused on letting workers tell their own story, and that general concept informs the organization’s publications and videos, including a video on the closing of Edmonton’s Celanese plant and a recent book on the history of working people in Alberta. Given that focus on letting workers speak for themselves, how does an academic involved in the project go about interpreting what these workers have to say? This article reflects, from a critical insider’s point of view, the extent to which oral histories add new dimensions to Alberta’s labour history and the extent to which they may also create new mythologies because of reluctance either to critique or place in context the evidence of oral sources. All of this then raises the issue of how far an academic, trained to plumb all discourses for more critical meanings, can go if they wish to be part of an endeavour in which they share with trade unionists an effort to extract meaning from labour’s past that is of use to working people’s struggles in the present and future.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.057 | 0.081 |
| Scholarly communication | 0.043 | 0.012 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 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".