Marksearch: Social Practice Art | Sue Mark (lecture, 73 minutes)
Bibliographic record
Abstract
Part of the Fall 2021 Colloquium (Place-Based Storytelling Techniques and Technologies)Commons Archive: Developing a Neighborhood Literacy“Commons Archive is a historical preservation program that re-thinks the archive as a place of privilege and de-colonizes our thinking about what’s worth preserving.”–Susan D. Anderson History Curator & Program Manager,California African American MuseumOakland-based cultural researcher Sue Mark will unpack strategies and questions surrounding Commons Archive, a creative grassroots history project she launched in 2015. Centered at North Oakland’s Golden Gate Library, Commons Archive has been providing platforms for longtime and new neighbors to narrate, describe and share their many histories. In 2010, Black neighbors were just under half of North Oakland’s population; eight years later, only a quarter of residents were Black. Today, the numbers are even lower. Commons Archive’s interactive format preserves neighbor knowledge that, if undocumented, will disappear.In collaboration with North Oakland, CA groups and organizations Commons Archives connects neighbors through stories, shared resources and celebrations. Commons Archive invites neighbors from all walks of life to express, sing, dance, read and listen to the multilayered stories that continue to shape these neighborhoods. By embracing traditional block club hospitality, Commons Archive supports community resiliency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.562 | 0.291 |
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".