Learning Deserts: Bold Transformation Plans Are Necessary If We Want to Prevent More Urban Communities from Becoming Learning Deserts
Bibliographic record
Abstract
[ILLUSTRATION OMITTED] If you've always lived near a grocery store, you may not know that many communities across the United States have limited or no access to foods needed to maintain a healthy diet. Known as food deserts, these communities are typically urban areas with a high level of racial segregation and large numbers of low-income households, or in rural areas. Residents are more likely to find fast-food restaurants or convenience stores than a grocery store (Imhoff and Pollan 2007). In a similar way, many of these same areas could be considered deserts--communities devoid of learning resources. These are communities that have many low-performing schools and a record number of school closures. In other words, they have limited or no access to the education needed to maintain a healthy lifestyle. According to the American Association of School Administrators, about 6% of schools were closed or consolidated in 2009-10, compared to 3% in 2008-09. AASA even predicts that 11% or more of the districts will close additional schools (Ellerson and McCord 2009). Dramatic school closures across urban districts this year have made major headlines. In Detroit, emergency financial manager Robert Bobb, who was sent in by the state to address declining enrollment and a budget deficit of more than $219 million, developed a to close a quarter of the city's 172 public schools over time. Detroit had previously closed 29 schools before classes started last fall. In that same month, Cleveland, Chicago, and Kansas City, Mo., also announced future school closings. In a 5-to-4 vote, the Kansas City school board decided to close nearly half of the district's 61 schools as a way to avoid bankruptcy. This doesn't include the unprecedented number of school closings in New York City since 2002 and continued downsizing in Denver, St. Louis, and Milwaukee. All of these closures stem from two problems: significant budget shortfalls as a result of declining enrollment and perpetual underperformance of multiple schools. For instance, Milwaukee Public Schools has closed about six schools each year since 2005 because of decreasing enrollment. Detroit's population has declined with each passing decade. The 2010 U.S. Census is expected to show that Detroit now has far fewer than 900,000 residents. Enrollment in the Detroit Public Schools has dropped from 164,500 in 2002-03 to 87,700 for the current school year--and is expected to decline further to 56,500 in 2014-15. Kansas City had only 18,000 students, projecting that 40% of its available seats are vacant at elementary schools, 60% are vacant in middle schools, and more than 60% are vacant in the high schools. Transforming Districts Cleveland has about 50,000 students, down from 78,000 over a decade ago, but most important, most of the city schools have been in academic emergency for multiple years. So, while Cleveland is planning to close one-third of its schools, district CEO Gene Sanders has made the school closings part of a larger transformation plan designed to make sure students are ready to graduate and compete for jobs, to have high-quality school choices in every neighborhood, to create measures of accountability for administrators, to attract as well as retain families and right-size the district's capacity (Crowder 2010). The went beyond declining enrollment and budget cuts but signaled hope and vision for teaching. Cleveland created the Office of New and Innovative Schools, supported in concept and cost by the Cleveland and George Gund foundations. The was comprehensive and schools are categorized for different actions--Growth, Refocus, Repurpose, Close, Open, or Relocate. Schools will be managed differently depending on their action category. The Cleveland Plan seeks to spread innovation, opening new proven school models and partnering with proven providers. The high school strategy, for example, will focus on breaking down struggling comprehensive high schools into academies that will serve 400 to 600 students and offer a portfolio of choices in all three regions of the city. …
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 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".