From Industrial Garden to Food Desert: Demarcated Devaluation in the Flatlands of Oakland, California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A dilapidated liquor store stands at the corner of 17th and Center in West Oakland. With its plastic sign cracked and yellowed, its paint pockmarked and peeling away in long lesions from the store’s warped clapboard siding, it could be a cliched metaphor for the decay of America’s “inner cities” during the postindustrial era (figure 5.1). But it is also representative of the disproportionate number of liquor stores in urban communities of color. Establishments such as these often serve as the sole food retailer in areas that planners and food justice activists have come to call “food deserts.” A recent report to Congress by the USDA Economic Research Service defines food desert as an area “with limited access to affordable and nutritious food, particularly such an area composed of predominately lower income neighborhoods and communities” (USDA 2009). A number of articles and reports over the last few years have attempted to characterize and identify food deserts in the United States, Canada, Britain, and Australia. Most have concluded that in the United States, food deserts disproportionately impact people of color (Smoyer-Tomic, Spence, and Amrhein 2006; Beaulac, Kristjansson, and Cummins 2009). While many studies have drawn spatial or statistical correlations or both between race and the absence of supermarkets (Raja, Ma, and Yadav 2008; Lee and Lim 2009; Zenk et al. 2005), researchers have also found that small corner stores and ethnic grocers are abundant in these food deserts (Short, Guthman, and Raskin 2007; Raja, Ma, and Yadav 2008). Nevertheless, fresh and nutritious produce is rarely available at these small stores, and the type of food generally tends to be of poorer quality and less healthy, high in sugars and saturated fats (Cummins and MacIntyre 2002). Food access in Oakland’s food deserts falls under a similar rubric. The socioeconomic terrain demarcating poverty and affluence in this Bay
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it