Gathering Stories for Community Action
Bibliographic record
Abstract
In October 2021, the International Autobiography Association Chapter of the Americas (IABAA) presented an online conference “Stories of Change, Stories for Change,” co-hosted by IABAA and the Faculty of Arts Signature Area “Stories of Change” at the University of Alberta (Canada). The conference featured speakers from across the Americas on autobiographical storytelling. The plenary panel for the conference featured four scholars in North America and the Caribbean engaged in different projects of mass listening, scholars doing critical work engaging with people and stories in order to create change in their communities. In order to continue the impact of that wonderful session, we invited the panelists from that session to reflect a bit more on that discussion to be included in this special issue cluster. Three of the panelists were able to join Laura Beard to pick up that discussion of their projects and the crucial ways in which stories help move us, as Marcy Schwartz points out, “from the micro to the macro, from the personal and intimate scene to the social and political expanse of human experience in the world.”
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".