Bibliographic record
Abstract
Archives are not neutral spaces; the memories and stories of minority communities are often overlooked and underrepresented. Narratives are symbolic representations of the past, and it is vital that a diversity of stories and memories is represented. Likewise, ephemeral artifacts, such as political buttons, are largely marginalized as objects of study because researchers tend to favor evidence found through written, textual documents. However, political buttons are significant because of their role as political memorabilia, marking a nation’s socio-political past, and their role as a narrative tool. Behind every political button, there is a potential narrative or story that is not often told. This article will attempt to address the lack of literature on political buttons in a Canadian context as well as give voice to counter-memory of the women’s movement in Canada and the experience of Blacks organizing for racial equality. Using a case study of selected political buttons from the archival collection of Jean Augustine, the first Black female Member of Parliament in Canada, stories of gender and racial equality rights activism in Canada, told by Jean Augustine, will be examined and contextualized. In particular, buttons representing stories about the National Black Coalition of Canada, the Congress of Black Women of Canada, and the anti-apartheid movement will be explored.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.087 |
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".