Lost Classroom, Lost Community: Catholic Schools' Importance in Urban America (2014) by Margaret F. Brinig and Nicole Stelle Garnett
Bibliographic record
Abstract
Lost Classroom, Lost Community: Catholic Schools' Importance in Urban America continues a line of scholarly work that calls attention to the invaluable effects of Catholic schools in the United States, in light of Catholic school closures and the rise of charter schools.It is unique in that it underlines the role of Catholic schools as institutions that contribute to the stability and health of the broader community in which they are located.Catholic schools make this contribution, the authors argue, through the generation of social capital, or "networks that make urban neighborhoods function smoothly" (p.113).The authors build on research about the educational benefits of social capital within a school (Bryk, Lee, & Holland, 1992), and the benefit for schools of social capital in the surrounding neighbourhood (Coleman, 1988), to propose that the social capital generated by Catholic schools extends well beyond the walls of the classroom.The authors highlight a statistically significant relationship between Catholic school closures and crime rates, drawing from survey data collected by the Project on Human Development in Chicago, Illinois, and neighbourhood police beat data.Between 1999 and 2005, Chicago experienced a significant decrease in crime, but the decrease was more pronounced in those neighbourhoods with open Catholic schools.Brinig and Garnett were able to successfully replicate their study in Philadelphia, Pennsylvania.However, they could not establish a similar pattern in the Archdiocese of Los Angeles, California, where very few Catholic schools have been closed.An open Catholic school there does not appear to affect crime rates.Los Angeles is unique because its Catholic schools were systematically established between 1948 and 1970 in a centralized manner to provide space for the tens of thousands of children who were annually denied entrance.As a result, the authors suggest that the impact of Catholic schools on crime is more evident in dense urban neighbourhoods and in neighbourhoods where schools emerged amid unplanned urban development, like Chicago and Philadelphia.Brinig and Garnett understand and do not equivocate on the limitations of their statistical analysis.They acknowledge the lack of uniformity in results, possible discrepancies related to limited and/or skewed statistical data, and the strong correlation of demographic and economic variables (such as a rise in poverty, escalation of the minority population, or overall decline in population) with social stability in a cityscape.Despite these limitations, the authors strive to isolate the effects of more pertinent factors to ascertain the value created by open Catholic schools in a community.While this weakens the strength of their findings, making it impossible 318
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".