Opting Out in the Empire State: A Geographic Analysis of Opting Out in New York, Spring 2015 & 2016

2020 ◽  
Vol 122 (2) ◽  
pp. 1-24
Author(s):  
Kathryn P. Chapman ◽  
Lydia Ross ◽  
Sherman Dorn
Keyword(s):  
New York ◽  
Population Density ◽  
Geographically Weighted Regression ◽  
Child Poverty ◽  
New York State ◽  
District Level ◽  
Weighted Regression ◽  
York State ◽  
Opting Out ◽  
Opt Out

Background Recently, states have experienced widely varying participation in annual assessments, with the opt-out movement concentrated in New York State and Colorado. Geographic variation between and within states suggests that the diffusion of opting out is multilayered and an appropriate phenomenon to explore geographic dimensions of social movements in education. Purpose The study analyzes the geographic patterns of opting out from state assessments in school districts in New York State. Research Design We conducted linear regression and geographically weighted regression on district-level proportions of third- through eighth-grade students in local public school districts for 2015 and 2016 (n = 623), excluding New York City and charter schools. Independent variables included the district-level proportion of students with disabilities, identified as English Language Learners, and identified as White; census-based small-area child poverty estimates for the districts; and the geographic population density of the district. Linear regressions excluded racial and ethnic dummy variables to reduce collinearity problems, and geographically weighted regression limited geographically varying coefficients to child poverty and population density based on preliminary analyses. Findings The unweighted ordinary least squares (OLS) of district-level opting out in both spring 2015 and spring 2016 are weakly predictive as a whole (adjusted R2 < .20). In both years, population density was a statistically significant but low-magnitude predictor of change in opt-out behavior using OLS. The proportion of students with Individualized Education Plans was positively associated with opt-out behavior, and district-level child poverty was negatively associated with opt-out behavior. The proportion of White students was a statistically significant positive predictor of opt-out behavior in spring 2015 but not statistically significant for 2016, though with a coefficient in the same direction (positive). Analyzing the same data with geographically weighted regression more than doubled the adjusted R2 for each year and demonstrated that there were areas of New York State where the coefficients associated with child poverty and population density reversed direction, with suburban Long Island and the western upstate region as areas with a magnified negative association between district-level child poverty and opting-out percentages. Conclusions In the past five years, social networks have enabled the long-distance organizing of social and political movements in education, including opting-out and teacher walkouts. However, the long-distance transmission of ideas does not explain intrastate variations. In this study, geographically weighted regression revealed the local variations in relationships between opting-out and two key variables. Local networks still matter critically to social organizing around education.

The Opt-Out Movement in New York: A Grassroots Movement to Eliminate High-Stakes Testing and Promote Whole Child Public Schooling

2021 ◽  
Vol 123 (5) ◽  
pp. 1-22
Author(s):  
Zhe Chen ◽  
David Hursh ◽  
Bob Lingard
Keyword(s):  
New York ◽  
Social Movements ◽  
Standardized Tests ◽  
Long Island ◽  
New York State ◽  
High Stakes Testing ◽  
Whole Child ◽  
High Stakes ◽  
York State ◽  
Opt Out

Purpose Over the last five years, approximately 50% of the students in Nassau and Suffolk counties on Long Island and 20% across New York State have opted out of the yearly standardized tests for third through eighth grade. This article focuses on two grassroots organizations, New York State Allies for Public Education (NYSAPE) and Long Island Opt Out (LIOO), the two parents who have been central to the organizations’ success, and the strategies and tactics that the two organizations have adopted to achieve such a high opt-out rate in New York. Context Since the passage of the No Child Left Behind Act of 2001 (NCLB), third through eighth grade public school students have been required to take yearly standardized tests. The most recent version of the exams focused on assessing students, their teachers, and schools based on the Common Core State Standards. Many educators and parents have argued that the standards and assessments negatively affect student learning. In response, educators, parents, teachers, and students have lobbied and publicly testified in an effort to reduce the length of the exams, if not eliminate them. However, the testimonies have had almost no impact on the policymakers. Consequently, some parents concluded that the only way to influence policymakers is to get enough students to opt out of the tests so that the scores were not valid and thus could no longer be used to compare students and teachers within and across schools for accountability purposes. Research Design This study is drawn from a qualitative research project in which we conducted interviews to understand how the opt-out movement developed and the strategies it adopted in relation to high-stakes testing in New York. The interviews with two parent leaders from NYSAPE and LIOO are the main data source for this article. Findings NYSAPE and LIOO can be characterized as real grassroots social movements in that all members have input in the goals and organizing strategies, and unpaid leaders emerge from the membership. Further, because the organizations lack permanent funding, they have to be innovative in using media. By motivating and empowering others and using social media such as Facebook and Twitter, they built a large network and a strong base so that they could influence policymakers and respond quickly at the local and state levels. Conclusion Their organizing strategies exemplified the participatory and grassroots nature of the new social movements as theorized by McAlevey. The opt-out movement is pushing back not only against high-stakes testing but also against the larger neoliberal construction of parents as simply consumers of schooling, rather than as active, informed citizens. The movement also supports whole-child schooling.

Text2Connect: a health system approach to engage tobacco users in quitline cessation services via text messaging

2020 ◽  
Vol 10 (1) ◽  
pp. 292-301 ◽  
Author(s):  
Paul Krebs ◽  
Scott E Sherman ◽  
Hannah Wilson ◽  
Omar El-Shahawy ◽  
Lorien L Abroms ◽  
...  
Keyword(s):  
New York ◽  
Health Systems ◽  
Text Messaging ◽  
Low Cost ◽  
Message Framing ◽  
New York State ◽  
Response Rates ◽  
Text Messages ◽  
Opting Out ◽  
Opt Out

Abstract Mobile technology has created the opportunity for health systems to provide low cost tobacco cessation assistance to patients. The goal of the present study was to examine the feasibility and effectiveness of an intervention (Text2Connect) that uses text messages to offer proactive connection to the New York State Smokers’ Quitline. The electronic health record at two urban health systems was queried for patients who were current smokers and who had an outpatient visit between March 2015 and February 2016. Smokers (N = 4000) were sent an informational letter. Those who did not opt out (N = 3719) were randomized to one of 6 message sequences in order to examine the effect of theoretically informed message frames on response rates. Participants were sent a series of text messages at baseline and at 1 month and were asked to reply in order to be contacted by the state quitline (QL). After removing 1403 nonworking numbers, texts were sent to 2316 patients, and 10.0% (205/2060) responded with a QL request. Almost one quarter (23.6%, 486/2060) replied STOP and 66.4% (1369/2060) never responded. QL request rates were significantly higher when response efficacy messages were not used (p &lt; .001). There were no differences by message framing on STOP requests (p &gt; .05). The Text2Connect intervention was well accepted with a minority opting out. A 10% QL response rate is noteworthy given that only 5–7 brief outreach text messages were used. Results indicate that simple self-efficacy-focused messaging is most effective at supporting response rates.

White Privilege and Power in the NYS Opt-Out Movement

2021 ◽  
Vol 123 (5) ◽  
pp. 1-18
Author(s):  
Rosa L. Rivera-Mccutchen
Keyword(s):  
New York ◽  
Critical Race Theory ◽  
White Privilege ◽  
New York State ◽  
High Stakes Testing ◽  
Critical Race ◽  
Race Theory ◽  
High Stakes ◽  
York State ◽  
Opt Out

Background Part of a special issue on the high-stakes testing opt-out movement, this article focuses its analysis on the movement within New York State, and examines white privilege and power within one specific organization, the NYS Allies for Public Education (NYSAPE). Specifically, I examine how the public-facing work of NYSAPE addressed (or ignored) race and/or racism in their efforts to resist high-stakes testing. I also ask, in what ways do their public stances affirm and reinforce white privilege and power? Purpose I explore the opt-out movement in New York State, and argue that it is a movement that has been largely dominated by white privilege and power. Employing critical race theory as analytical and methodological tools, I briefly examine the development and policy positions of NYSAPE, a coalition of grassroots parent, educator and community organizations. Research Design This qualitative case study focuses on NYSAPE and employs critical race theory as a methodological and analytical framework, with specific emphasis on whiteness as property (power) and interest convergence. Conclusions/Recommendations The paper aims to engage the opt-out movement in considering how its (in)actions are shaped by racism, a deeply entrenched element in our society, and pushes the movement to take a more liberatory stance for all children. Leaders within the opt-out movement, particularly in predominantly white and middle- to upper-class communities, have to examine their complicity in perpetuating racial inequities.

Population density and cancer incidence differentials in New York State, 1978?82

1992 ◽  
Vol 3 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Philip C. Nasca ◽  
Martin C. Mahoney ◽  
Patricia E. Wolfgang
Keyword(s):  
New York ◽  
Population Density ◽  
Cancer Incidence ◽  
New York State ◽  
York State

Association of census tract-level incarceration rate and life expectancy in New York State

2021 ◽  
pp. jech-2020-216077
Author(s):  
Louisa W Holaday ◽  
Benjamin Howell ◽  
Keitra Thompson ◽  
Laura Cramer ◽  
Emily Ai-hua Wang
Keyword(s):  
New York ◽  
Population Density ◽  
Life Expectancy ◽  
Census Tract ◽  
Fixed Effects ◽  
New York State ◽  
York State ◽  
Incarceration Rate ◽  
Census Tract Level ◽  
Improve Life Expectancy

BackgroundJail incarceration rates are positively associated with mortality at the county level. However, incarceration rates vary within counties, limiting the generalisability of this finding to neighbourhoods, where incarceration may have the greatest effects.MethodsWe performed a cross-sectional analysis of census tract-level state imprisonment rates in New York State (2010) and life expectancy data from the US Small-area Life Expectancy Estimates Project (2010–2015). We modelled fixed-effects for counties and controlled for tract-level poverty, racial makeup, education, and population density from the American Community Survey (2010–2014), and violent crime data from the New York City Police Department (2010). We also examined interactions between incarceration rate and poverty, racial makeup, and population density on life expectancy.ResultsLife expectancy at the highest quintile of incarceration was 5.5 years lower than in the lowest quintile, and over 2 years lower in a fully-adjusted model. Census tract-level poverty and racial makeup both moderated the association between incarceration and life expectancy.ConclusionCensus tract-level incarceration is associated with lower life expectancy. Decarceration, including alternatives to incarceration, and release of those currently incarcerated, may help to improve life expectancy at the neighbourhood level.

Population Density and Cancer Mortality Differentials in New York State, 1978–1982

1990 ◽  
Vol 19 (3) ◽  
pp. 483-490 ◽  
Author(s):  
MARTIN C MAHONEY ◽  
DANIELLE S LABRIE ◽  
PHILIP C NASCA ◽  
PATRICIA E WOLFGANG ◽  
WILLIAM S BURNETT
Keyword(s):  
New York ◽  
Population Density ◽  
Cancer Mortality ◽  
New York State ◽  
York State ◽  
Mortality Differentials

Resisting the neoliberal: parent activism in New York State against the corporate reform agenda in schooling

2019 ◽  
pp. 89-102
Author(s):  
David Hursh ◽  
Sarah McGinnis ◽  
Zhe Chen ◽  
Bob Lingard
Keyword(s):  
New York ◽  
New York State ◽  
High Stakes Testing ◽  
Democratic Citizenship ◽  
High Stakes ◽  
York State ◽  
Reform Agenda ◽  
Opt Out ◽  
Corporate Reform ◽  
Political Success

Over the last two decades, parents and community members in New York have increasingly resisted the neoliberal corporate reform agenda in schooling, including rejecting high-stakes testing. The parent-led opt-out movement in New York State has successfully opted around 20% of eligible students out of the Common Core state standardized tests over the last three years. To understand how a parent-led grassroots movement has achieved such political success, this chapter focuses on the two most influential opt-out organizations in New York State, the New York State Allies for Public Education (NYSAPE) and Long Island Opt Out (LIOO). The chapter investigates how they used social media and horizontal grassroots organizing strategies to gain political success, along with vertical strategies pressuring the legislature and Board of Regents. Our research reveals that parents in New York are reclaiming their democratic citizenship role in influencing their children’s public schooling and rejecting the corporate reform agenda.

scholarly journals 467. Spatial-Temporal Prediction Model of COVID-19 Mortality across 62 Counties in New York State

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S300-S300
Author(s):  
Yunyu Xiao ◽  
Chengbo Zeng
Keyword(s):  
New York ◽  
Population Density ◽  
Large Population ◽  
New York State ◽  
County Level ◽  
Mixed Effect ◽  
Cumulative Mortality ◽  
York State ◽  
Temporal Prediction ◽  
Public Data

Abstract Background COVID-19 pandemic has resulted in considerable morbidity and mortality. New York State (NY) is the hotspot with most coronavirus cases, while there are spatial/temporal variations. Yet, few examined county-level factors of mortality in COVID-19 patients in NY. Based on the sociological framework in health, this study links large and representative public data to understand COVID-19 mortality in NY over different stages of pandemic. Methods Mortality cases were from Mar 17 (state of emergency; 0.1 per 100,000), Apr 18 (coronavirus peak; 87.4), Apr 25 (expand testing; 108.7), and May 11 (daily reduced to original; 137.6). Three domains (compositional, contextual, and collective) and 28 county-level predictors of mortality were extracted from American Community Survey, Area Health Resources, US Crime Data, and Religious Data systems for each county. Compositional domain covered socio-demographic characteristics in local areas (e.g., age, sex, race/ethnicity, housing). Contextual domain covered include social and physical opportunities (e.g., health insurance coverage, transportation, mental health providers). Collective domain covered neighborhood safety and religious adherents. Mixed effect regression with the least absolute shrinkage selection operator (LASSO) was used to select the predictors and estimate the parameters after adjusting the time effect and cumulative prevalence of COVID-19. 有道词典 ; 0.1 per 100,000 people 详细X ;每100000人0.1 Results NYC and the nearby boroughs (i.e., Bronx, Kings, Manhattan, Queens) had the highest cumulative mortality (231.69 per 100,000 people). Counties far from New York Cities (e.g., Allegany, Cortland, Delaware) had the lowest cumulative mortality. Spatial variation showed counties with larger population density (β=.01, p=.022) and/or higher proportion of people with at least high school education (β=227.24, p=.03) were at risk of higher cumulative mortality in COVID-19. Conclusion Unique spatial clustering mortality risk of COVID-19s was detected, highlighting important but understudied roles of contextual and collective factors. Tailored policy efforts shall be designed to support counties with large population density and high levels of education to prevent the mortality related to COVID-19 infection in NY. Disclosures All Authors: No reported disclosures

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