Bibliographic record
Abstract
Una, Saxon, and Arcadia are three neighborhoods in Spartanburg County, South Carolina undergoing an era of neighborhood re-organization, change, and development. As historic textile mill villages from the county’s age of industrialization, the Una, Saxon, and Arcadia neighborhoods today are characterized by privately-owned, mid-century mill homes and a high population of renters. Neighbors are concerned about the dilapidated housing stock falling into disrepair and the resulting impacts of abandoned and condemned properties. To advocate alongside Una, Saxon, and Arcadia residents for equitable neighborhood investment, our research team conducted two years of mixed-method ethnographic research across the three neighborhoods to determine the impacts of abandoned and condemned properties on neighborhood wellness. Through our research collaborations, our team identified deeply personal and political associations between residents, their homes, and their stake in the Una, Saxon, and Arcadia community. Advocating for equity in Una, Saxon, and Arcadia cannot be simplified to one policy recommendation or development plan. Rather, collective organization and engagement amongst residents bolstered by key stakeholders, such as the county, may provide an equitable and inclusive path to reimagining neighborhood futures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".