A place to belong: creating an urban, Indian, women-led land trust in the San Francisco Bay Area
Bibliographic record
Abstract
When grounded in Indigenous epistemologies, land trust structures provide an effective, inclusive vehicle to enact community and landscape care in the face of colonial disruptions. The Sogorea Te’ Land Trust in Lisjan (Ohlone) homelands in the San Francisco East Bay Area is the first Indigenous, women-led, urban land trust in the world. Two Indigenous women active in the Bay Area Indigenous community saw multiple community needs that coalesced around a lack of land. Without land, there is no place for grounded spiritual practice, cultivation and processing of foods and medicine, and recognition of the First Peoples of the San Francisco East Bay area. Without land, ongoing colonial relations perpetuate exclusion of Indigenous peoples and desecration of their sacred places. We explore the development, framing, application, and expansion of the Sogorea Te’ Land Trust as a vehicle for rematriating land and creating community in a diverse and dense urban Indigenous space. Through the Sogorea Te’ Land Trust, the potential, goals, and possibilities of land trusts are reimagined beyond conservation to inclusive eco-cultural-community restoration and well-being.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".