Bibliographic record
Abstract
Since the 1960s, if not before, oral history and working-class history have been a dynamic duo, complimenting and overlapping, but also challenging and questioning each other. Both lay and professional historians have been in the forefront of efforts to recuperate, interpret, and preserve the oral histories of working-class individuals and communities across the globe. They created written histories, archival collections, museum exhibits, and community projects that gave workers, their families, and their communities -those who were less likely to leave archival and written sources for posterity -a new voice, and a new place in history. Working-class oral history has also encompassed far more than recovery and preservation. Labour historians have enriched the field of oral history by addressing questions about method, theory, and approach, by offering critical reflections on our assumptions and expectations about oral history practice. Oral history has similarly enriched the field of working-class history, posing new questions, challenging existing interpretations, and encouraging the diversification of the themes and subjects we study. In recognition of this dynamic relationship, and the ongoing, mutually beneficial conversation between oral and working-class history, Oral History Forum commissioned this special issue. Periodizing and classifying the historiography of working-class oral history is not an easy task. It is always dangerous to talk about the origins of a turn towards the use of oral history, since there are inevitably antecedents to consider: folklorists, anthropologists and popular writers were all using oral history long before the 1960s, sometimes with the expressed purpose of preserving the voices of ‘ordinary’ working people. Eye witness recollections, as Paul Thompson noted decades ago, have long been a historical source; however, the increased emphasis put on archives and documents, as history professionalized in the late nineteenth century, did marginalize oral accounts. Nonetheless, after the Second World War in some countries, and certainly by late 1960s, there was a new openness towards oral history in the historical profession, as more practitioners embraced a method previously associated with the social sciences, especially anthropology, and journalism. As oral history assumed more
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.030 | 0.043 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 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".