Telling Our Stories/Animating Our Past: A Status Report on Oral History and New Media
Bibliographic record
Abstract
Tens of thousands of oral history interviews sitting in archival drawers, on computer hard drives, or on library bookshelves have never been listened to. Thousands of new \ninterviews are being added each year by the many large testimony projects now underway, including Canada’s Truth and Reconciliation Commission and the Historica–Dominion Institute’s Memory Project. Although the existence of these immense collections is widely known, the interviews are difficult to access. How can we combine oral history and new media to insure that the potential of such important projects is fully realized? Emergent and digital technologies are opening up new possibilities for accessing Canadian memories and transmitting them to various audiences. New forms of media are changing the ways we think about and do oral and public history. \n \nDes milliers d’entrevues d’histoire orale oubliées dans des tiroirs d’archives, sur des disques durs et sur des étagères de bibliothèque n’ont jamais été écoutées. En même temps, chaque année, de nouvelles entrevues viennent s’ajouter par milliers dans le cadre de grands projets de témoignage, y compris la Commission de vérité et réconciliation du Canada et le Projet Mémoire de l’Institut Historica Dominion. Bien que l’existence de ces collections immenses ne soit guère un secret, les entretiens sont difficiles d’accès. Comment peut-on combiner l’histoire orale et les nouveaux médias afin de réaliser pleinement le potentiel de projets si importants? Des technologies numériques récentes présentent de nouvelles possibilités pour accéder aux souvenirs canadiens et les transmettre à divers publics. En effet, de nouvelles formes de média sont en train de changer les manières de penser et de pratiquer l’histoire orale et publique.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".