Asymmetry in Protest Control? Comparing Protest Policing Patterns in Montreal, Toronto, And Vancouver, 1998-2004

2010 ◽  
Vol 15 (4) ◽  
pp. 489-509 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Regression Models ◽  
Management Practices ◽  
National Level ◽  
Management Model ◽  
Logistic Regression Models ◽  
Cooperative Relationships ◽  
Protest Policing ◽  
The 1960S ◽  
Bayesian Logistic Regression ◽  
Western Democracies

Scholars have argued that since the 1960s, protest policing in Western democracies has moved toward emphasizing cooperative relationships with challenging groups. This evolution is referred to as the negotiated management model of protest control. Much of the literature that informs this perspective is based on either analyses of a limited subset of demonstrations or from national-level observation. Few studies have examined whether negotiated management practices hold at city levels. This research examines city-level protest policing using 1,152 demonstrations occurring between 1998 and 2004 in Montreal, Toronto, and Vancouver, Canada. Bayesian logistic regression models are estimated using arrests as the response variable. The main findings suggest that only protestor premobilization and the police use of force are uniformly related to arrests; that there is considerable variation across cities; and that the larger pattern of results is not consistent with negotiated management practices.

scholarly journals A Longitudinal Study of Lameness Incidence and Association with Animal-Based Welfare Measures in Dairy Cows

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Mohammed Babatunde Sadiq ◽  
Siti Zubaidah Ramanoon ◽  
Wan Mastura Shaik Mossadeq ◽  
Rozaihan Mansor ◽  
Sharifah Salmah Syed-Hussain
Keyword(s):  
Dairy Cows ◽  
Body Condition ◽  
Regression Models ◽  
Management Practices ◽  
Cox Regression ◽  
Body Condition Score ◽  
Logistic Regression Models ◽  
Condition Score ◽  
Claw Lesions ◽  
Welfare Measures

The objective of this study was to investigate time to lameness event, prevalence of claw lesions, and their associations with animal-based welfare measures (ABWMs) in dairy cows. A total of 120 non-lame lactating cows (n = 30 each from four intensive dairy farms) having no claw lesion were enrolled in the study. The cow-level enrollment criteria included good body condition score (BCS), normal hock condition score, absence of body injuries, and normal claw length. Information on cows’ parity, milk yield, and previous lameness event (PLE) were recorded. The cows were observed twice monthly (every 2 weeks) for locomotion scores (LS), and ABWMs, whereas claw lesions were recorded upon onset of lameness and at the end of the observation period. Cows were considered lame when 2 consecutive LS = 3, or any assessment with score 4. Cox regression models were used to investigate lameness incidence and the association with ABWMs, while association between the latter and claw horn lesions (CHL) prevalence were analyzed using logistic regression models. Twenty-four percent (29/120) of the cows were lame during the study period. Lameness risk was associated with PLE (Hazard ratio; HR = 7.4; 95% CI 2.4-23.0), presence of overgrown claw (HR = 3.7; 95% CI 1.1-12.6) and low BCS pre-lame (HR = 4.5; 95% CI 1.3-16.6). Amongst the cows affected with claw lesions, 68.4% (37/44) were lame and CHL were predominant (75.4%) compared to infectious claw lesions (24.7%). Cows with lower BCS (< 3.0) (OR = 5.7; 95% CI 1.6- 20.4) and those with PLE (OR = 7.2; 95% CI = 2.1-24.7) were more likely to have CHL. Management practices such as maintaining cows’ body condition, improved care for those with history of lameness, and proper claw trimming could assist farmers to reduce lameness incidence in the studied herds.

On the existence of posterior mean for Bayesian logistic regression

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Huong T. T. Pham ◽  
Hoa Pham
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Sufficient Conditions ◽  
Maximum Likelihood Estimates ◽  
Logistic Regression Models ◽  
Posterior Mean ◽  
Different Types ◽  
Data Points ◽  
Necessary And Sufficient ◽  
Bayesian Logistic Regression

Abstract Existence conditions for posterior mean of Bayesian logistic regression depend on both chosen prior distributions and a likelihood function. In logistic regression, different patterns of data points can lead to finite maximum likelihood estimates (MLE) or infinite MLE of the regression coefficients. Albert and Anderson [On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 1984, 1, 1–10] gave definitions of different types of data points, which are complete separation, quasicomplete separation and overlap. Conditions for the existence of the MLE for logistic regression models were proposed under different types of data points. Based on these conditions, we propose the necessary and sufficient conditions for the existence of posterior mean under different choices of prior distributions. In this paper, a general wide class of priors, which are informative priors and non-informative priors having proper distributions and improper distributions, are considered for the existence of posterior mean. In addition, necessary and sufficient conditions for the existence of posterior mean for an individual coefficient is also proposed.

scholarly journals Modeling and mapping wildfire ignition risk in Portugal

2009 ◽  
Vol 18 (8) ◽  
pp. 921 ◽  
Author(s):  
Filipe X. Catry ◽  
Francisco C. Rego ◽  
Fernando L. Bação ◽  
Francisco Moreira
Keyword(s):  
Spatial Patterns ◽  
Regression Models ◽  
Good Accuracy ◽  
National Level ◽  
Risk Map ◽  
Wildfire Management ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Fire Ignitions ◽  
Southern European Countries

Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict the spatial patterns of ignitions constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. In this study, we analyzed 127 490 ignitions that occurred in Portugal during a 5-year period. We used logistic regression models to predict the likelihood of ignition occurrence, using a set of potentially explanatory variables, and produced an ignition risk map for the Portuguese mainland. Results show that population density, human accessibility, land cover and elevation are important determinants of spatial distribution of fire ignitions. In this paper, we demonstrate that it is possible to predict the spatial patterns of ignitions at the national level with good accuracy and using a small number of easily obtainable variables, which can be useful in decision-making for wildfire management.

scholarly journals Farm Exit Among Smallholder Farmers of Nepal: A Bayesian Logistic Regression Models Approach

2020 ◽  
Vol 9 (4) ◽  
pp. 675-683
Author(s):  
Keshav P. Pokhrel ◽  
Taysseer Sharaf ◽  
Prem Bhandari ◽  
Dirgha Ghimire
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Smallholder Farmers ◽  
Logistic Regression Models ◽  
Bayesian Logistic Regression

scholarly journals Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Navid Feroze ◽  
Muhammad Ajmal Ziad ◽  
Rabia Fayyaz ◽  
Yaé Ulrich Gaba
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Healthcare Services ◽  
Maternal Healthcare ◽  
Logistic Regression Models ◽  
Socioeconomic Groups ◽  
Bayesian Logistic Regression ◽  
Skilled Health ◽  
Better Than

Objectives. This study is aimed at investigating the time trends and disparities in access to maternal healthcare in Pakistan using Bayesian models. Study Design. A longitudinal study from 2006 to 2018. Methods. The detailed analysis is based on the data from Pakistan Demographic and Health Survey (PDHS) conducted during 2006-2018. We have proposed Bayesian logistic regression models (BLRM) to investigate the trends of maternal healthcare in the country. Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (CLRM). Results. The results from the analysis suggested that BLRM perform better than CLRM. The access to antenatal healthcare increased from 61% to 86% during years 2006-18. The utilization of medication also improved from 44% in 2006 to 60% in 2018. Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018. Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery. Additionally, the likelihood of MHS provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of Sindh and Punjab. Conclusions. The gaps in utilization of MHS in different socioeconomic groups of the society have not decreased significantly during 2006-2018. Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States

2007 ◽  
Keyword(s):  
Logistic Regression ◽  
Chesapeake Bay ◽  
Regression Models ◽  
Management Practices ◽  
Fish Habitat ◽  
Virginia Institute ◽  
Predictive Ability ◽  
The United States ◽  
Carcharhinus Plumbeus ◽  
Logistic Regression Models

<em>Abstract.</em>—The lower Chesapeake Bay is the largest summer nursery for sandbar sharks <em>Carcharhinus plumbeus </em>in the western Atlantic. The objective of this study was to define essential fish habitat for juvenile sandbar sharks in this estuary. The longline survey conducted by the Virginia Institute of Marine Science was expanded from 1990 to 1999 to include ancillary stations throughout the Virginia portion of Chesapeake Bay to delineate this nursery spatially. We analyzed catch per unit of effort data from 83 stations as a function of nine physical and environmental variables using tree-based regression models. The highest abundance of juvenile sandbar sharks was predicted where salinity was greater than 20.5 (practical salinity scale) and depth was greater than 5.5 m. The models also suggested that dissolved oxygen concentration may influence shark distribution. To increase applicability of the models to management practices, we introduced distance to the mouth of the estuary as a surrogate variable for salinity. The models estimated that the highest abundance of sharks was in areas less than 34.5 km from the mouth of the estuary and in depths greater than 5.5 m. The areas of the estuary that met the criteria of the models, based on the threshold values of the variables, were mapped spatially in a geographic information system. The resulting response surfaces were interpreted to represent essential nursery habitat for sandbar sharks in Chesapeake Bay. Both models performed very well using several dependent and independent measures to estimate their classification and predictive ability. We used logistic regression with presence/absence data to validate the tree models. The logistic regression models agreed very well with the tree-based regression models, selecting the same variable combinations to predict sandbar shark presence and absence.

Is anti-consumption driving meat consumption changes in Australia?

2019 ◽  
Vol 121 (1) ◽  
pp. 123-138 ◽  
Author(s):  
Lenka Malek ◽  
Wendy Umberger ◽  
Ellen Goddard
Keyword(s):  
Household Income ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
National Level ◽  
Meat Consumption ◽  
Personal Health ◽  
Online Questionnaire ◽  
Content Type ◽  
Logistic Regression Models ◽  
Consumption Decisions

Purpose The purpose of this paper is to evaluate recent changes made by Australian consumers in their consumption of beef, chicken, pork and lamb, as well as the factors motivating both decreased and increased consumption of each type of meat. Reasons for meat-avoidance are also examined. Design/methodology/approach An online questionnaire was completed in July 2016 by two Australian samples comprising: adults from the general population; and vegetarians. Data were analysed for 287 meat consumers and 82 meat avoiders. Descriptive statistics and results of multinomial logistic regression models are presented. Findings Meat consumers most commonly reported reducing consumption of beef in the last 12 months (30 per cent); followed by lamb (22 per cent), pork (14 per cent) and chicken (8 per cent). The following factors were associated with reductions in meat consumption: concerns regarding price and personal health; age and household income; and food choice motivations related to personal benefits, social factors and food production and origin. Main reasons motivating meat-avoidance were concerns regarding animal welfare, health and environmental protection. Originality/value This is the first Australian study providing national-level insight on how and why meat consumption patterns are changing. Reasons for changes are examined through an anti-consumption lens, investigating rationale for avoiding, reducing and increasing consumption. This provides a more comprehensive understanding of meat consumption and anti-consumption decisions, which are becoming increasingly complex. Insights on the psychologically distinct motivations underpinning avoidance, reductions and increases in meat consumption can inform the development of strategies aimed at promoting a societal-shift towards consumption of more sustainable dietary protein sources.

scholarly journals Five risk factors and their interactions of probability for a sow in breeding herds having a piglet death during days 0–1, 2–8 and 9–28 days of lactation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yuzo Koketsu ◽  
Ryosuke Iida ◽  
Carlos Piñeiro
Keyword(s):  
Regression Models ◽  
Management Practices ◽  
Gestation Length ◽  
Early Lactation ◽  
Logistic Regression Models ◽  
Five Factors ◽  
More Care ◽  
Parity Number ◽  
Multi Level ◽  
Piglet Mortality

Abstract Background Increasing preweaning piglet mortality is a concern for veterinarians and producers in relation to sow performance and piglet welfare. Our objectives were (1) to characterize pre-weaning piglet mortality risk for sows (PWM) during early (0–1 days), mid- (2–8 days) and late (9–28 days) lactation and (2) to quantify the following five factors and their interactions, parity, number of piglets born alive (PBA), number of stillborn piglets (SB), gestation length (GL) and season for PWM during the three lactation phases. Methods Data obtained from 264,333 parity records of 55,635 sows farrowed in 2015 and 2016 from 74 Spanish herds. Three multi-level mixed-effects logistic regression models were separately applied for PWM during three lactation phases, which was analyzed as whether or not a sow had a piglet death (i.e. probability of a sow having a piglet death) in each phase. Results PWM during early, mid- and late lactation were 36.9, 27.0 and 15.4%, respectively. As PBA increased from 11 or less to 16 or more pigs, PWM during early and mid-lactation increased by 15.8 and 6.0%, respectively, but there was no increase during late lactation. Also, as GL decreased from 117–120 to 110–113 days, PWM during early, mid- and late lactation increased by 7.5, 6.8 and 1.5%, respectively. Additionally, PWM during the respective lactation phases increased by 8.3, 5.2 and 1.0%, as SB increased from 0 to 3 or more pigs. During early lactation, parity 1 sows had 2.1% lower PWM than parity 5 or higher sows, but during mid- and late lactation they had 4.2% higher PWM (P < 0.05). However, there was no difference between summer and winter for PWM during early lactation (P = 0.26). Conclusion Management practices to reduce PWM need to take account of these factors, and be modified for different phases. For example, during early lactation special care should be given to piglets born to parity 5 or higher sows farrowing 16 or more PBA, having 3 or more SB or GL 110–113 days, whereas during mid- and late lactation more care should be given to piglets born to parity 1 sows with the same PBA, GL and SB conditions.

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