Threads of Change: Oral History in Action
Bibliographic record
Abstract
Hear, Here: Making Oral History Active History www.hearherelacrosse.org www.hearherelondon.org Hear, Here is an Canadian and American audio-documentary project that brings oral history onto the streets. Orange street-level signs with phone numbers indicate where stories occured. Users call the number to hear a story of the exact location where they stand, and can choose to leave a new story about that location or any other. Many stories told by historically underrepresented groups challenge the traditional narratives of cities which often focus on prosperity and whiteness. In both countries we have chosen gentrifying neighborhoods and seek to identify the uniqueness of the neighborhood before its traditional occupants are displaced. The stories help stakeholders see the importance of the diversity of the neighborhood and therefore strategize to preserve the socioeconomic and racial diversity.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".