COMBINING THE ETHICS AND SCIENCE
OF DISASTER MANAGEMENT: KEY
ISSUES, POLICY CONSIDERATIONS
AND BEST PRACTICES
Jason Levy
University of Hawaii, U.S.A.
E-mail: jlevy@hawaii.edu
Bishwajeet Pandey
Director Gyancity Research Lab.
E-mail: gyancity@gyancity.com
Recepción: 01/08/2019 Aceptación: 23/09/2019 Publicación: 06/11/2019
Citación sugerida:
Levy, J. y Pandey, B. (2019). Combining the ethics and science of disaster management:
key issues, policy considerations and best practices. 3C Tecnología. Glosas de innovación
aplicadas a la pyme. Edición Especial, Noviembre 2019, 233-251. doi: http://dx.doi.
org/10.17993/3ctecno.2019.specialissue3.233-251
Suggested citation:
Levy, J. & Pandey, B. (2019). Combining the ethics and science of disaster management:
key issues, policy considerations and best practices. 3C Tecnología. Glosas de innovación
aplicadas a la pyme. Speciaal Issue, November 2019, 233-251. doi: http://dx.doi.
org/10.17993/3ctecno.2019.specialissue3.233-251
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
234
235
ABSTRACT
Around the world, the scientic discourse about disasters has historically focused
predominantly upon the characteristics of the physical hazards themselves and the
costs of mitigation actions to reduce the risks of these physical threats. While this
physical science approach involves the expertise of talented experts from the elds of
seismology, hydrology and geophysics the importance of addressing the root causes
of disasters has become increasingly recognized in the social sciences. For example, in
the broader public and policy literature, there are important ethical and social disaster
issues which include understanding the root causes of community vulnerabilities
and the ethical issues of not addressing climate change impacts (otherwise known
as the costs of inaction). This research uses statistical tools including binary logistic
regression to analyze ethical considerations in ood disaster management issues.
Since many of Asia’s worst oods have occurred in China they are used as a case
study.
KEYWORDS
Disaster Management, Key Issues, Policy, Ethics and Science, Catastrophe.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
234
235
1. INTRODUCTION
Ethical decisions are key to shaping the disaster studies eld and they must be included
in the online classroom: It is important that values and ethical considerations are made
explicit during the disaster planning process so that under conditions of pressure
of urgency and criticality they can be made consistent with the ethical judgments
that underlies the emergency management decision process. In the community
disaster planning phase, there will be innumerable issues, each with its own ethical
components. What humans and assets do we protect, and to what level of safety?
How do we set disaster budgets and priorities? Answers explored during disaster
preparedness activities should be based on key values and ethical analysis that can
provide guidance during implementation Other issues include:
• How do we ensure that priority-setting judgments are not purely technical
matters?
• How do we ensure the goals of transparency and accountability?
• Who should receive the most resources?
• Who do we rescue rst?
• How do deal with families that refuse to follow mandatory evacuation orders?
• When do we stop expending resources to critically ill victims that are unlikely
to survive?
• When do we phase out rescue eorts and shift to recovery mode?
The way these questions are answered reects the ethical perspectives and moral
analysis strategies of the planning group(s). In this paper we consider the role of
both ethical and scientic decisions in grave ood management challenges. Flood
events often constitute a catastrophic disaster threat: they have an enormous impact
on human wellbeing, jeopardizing important social development goals such as
addressing poverty, ensuring adequate food, water, and sanitation, and protecting
the environment. Direct losses from oods include drownings and injuries as well
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
236
237
as damage to infrastructure and property, agricultural production, and sites of
historical and cultural value. Indirect health problems often arise, such as water-
borne infections, exposure to chemical pollutants released into ood waters, and
vector-borne diseases.
2. 1998 YANGTZE RIVER FLOOD CASE STUDY
At 6,300 km (3,900 miles) the Yangtze is Asia’s longest river, the world’s third longest
and the most important waterway linking China’s leading commercial hub, Shanghai,
to the less industrialized regions of the Yangtze river basin: The fertile Yangtze River
basin supports 40% of China’s GDP (including 40% of the nation’s agricultural and
industrial output) and is home to one third of its people.. The Yangtze river basin is
also a vital source of natural resources, accounting for 40% of China’s freshwater
resources, more than 70% of the country’s rice and shery production and half of
its grain. The 1997-1998 El Nino and 1998 Asian summer monsoon was one of
the strongest on record, and monsoon rains continued unabated for much of the
summer. The central and southern parts of the country along the Yangtze river and
its tributaries were severely impacted by more than 60 days of heavy ooding in
the Yangtze River Valley. The Yangtze ooding constituted the world’s single most
devastating natural disaster in 1998 and China’s worst ooding in over 40 years:
approximately 200 million people were aected over 50 million acres as 670 mm of
precipitation occurred in the Yangtze river valley from June to August, 1998 (Samel &
Liang, 2003). In July and August 1998, extensive ooding also occurred in northeast
China, in the Songhuanjiang, Nenjiang and other rivers. The 1998 Yangtze oods
aected more than 180 million people, killing approximately 4,000, damaging more
than 10 million homes and forcing 14 million people to relocate. Direct economic
losses were put at 31 billion US dollars.
Four factors which signicantly worsened the impact of heavy rain during the 1998
ood were:
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
236
237
• Deforestation and overgrazing, sharply reducing the capacity of forests and
grasslands to retain water; Forest cover in Sichuan province fell from 20% of
the land area in the 1950s to 9% by the late 1970s (Kirby, 2001).
• Loss of lakes and wetlands, cutting the capacity of the river’s middle and lower
reaches to store water. The surface area of the lakes along the Yangtze shrunk
from 17,198 sq km in 1949 to only 6,605 sq km in 1980. In the early 1950s,
the Yangtze basin had contained 4,033 large and small lakes, of which about
1,100 were lost over the past half century.
• Rising erosion rates, causing rivers and wetlands to ll with silt. For example,
one of the largest reservoirs in Guangxi province, Changgang, has lost half a
million cubic metres of capacity annually as it silts up.
• Snowmelt and melting glaciers in the Qinghai-Tibetan plateau.
Both the amount of precipitation over the Yangtze river catchment and the
oodwater discharge from the upper basin of the Yangtze river did not exceed the
historical extremes during the 1998 ooding, but water levels in the middle basin
far exceeded the historical maximum. Historically, dikes have been built to control
ooding along the Yangtze River, but the 1998 ood levels in the middle reaches
of the Yangtze River forced Chinese ocials to consider dramatic strategies to save
large cities on the Yangtze River from inundation. During the summer of 1998, it
was feared that Yangtze River ooding would cause the dikes along the Yangtze to
fail to some degree, particularly those already weakened due to erosion, aging, or
neglected repairs.
To minimize the probability of a catastrophic dike failure in the densely populated
city of Wuhan (central China’s largest industrial center, with more than 7 million
residents in China’s central Hubei province) and neighboring farmland, Chinese
authorities deliberately destroyed dikes in Jianli County (Hubei province), about
90 miles upriver from Wuhan. This preventative action was successful in diverting
oodwaters away from Wuhan, lowering the height of the Yangtze River at Wuhan.
While this purposeful destruction of dikes at Jianli temporarily prevented Wuhan
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
238
239
from being ooded, the social and economic impact on Jianli Country was immense:
more than 500,000 people living adjacent to the Yangtze River were forced to
evacuate (many on extremely short notice).
However, Chinese ocials believed that saving Wuhan from inundation might also
require opening oodgates and the deliberate destruction of dikes in the Jingjiang
section of the Yangtze River which runs from Zhijiang (Hubei Province) to
Chenglingji (Hunan Province). It is one of the most hazardous parts of the Yangtze
River: the many bends in the river slow down the water and the riverbed is high due
to the resulting sand and mud deposits. The higher water level in the Jingjiang leads
to the ancient Chinese saying: “The danger of the Yangtze River lies in Jingjiang”.
The 180 km Jingjiang river embankment provides defense for the central Chinese
city of Wuhan and the major Beijing-Guanzhou Railway transportation artery. It
also protects the productive 30,000 km2 Jianghan plain (an alluvial plain located in
the middle and south of Hubei province which borders the Dongtinghu Plain and
has an area of more than 30 thousand square kilometers).
The Jingjiang ood plain lies in central Hubei province which is home to over 300,000
people. The Jingjiang ood diversion area had not been used since 1954, when oods
killed more than 30,000 people. However, purposefully destroying dikes at Jingjiang
would reduce the risk of dikes suddenly bursting at Shashi City. Accordingly, extensive
preparations were put in place to dynamite the Jingjiang dikes and divert waters into
the Jingjiang oodplain. This was expected to submerge more than 1,000 square
kilometers (386 square miles) of land and 27,000-33,000 ha (68,000-82,000 acres) of
farmland in the Jingjiang oodplain. Deliberately ooding towns and villages in the
Jingjiang area required the approval of the State Council of the People’s Republic
of China ((國務院), the chief administrative authority of the People’s Republic of
China. Since 1954 China’s State Council has been constitutionally identical to the
Central People’s Government (Chinese: 中央人民政府), particularly in relation to
local governments. China’s state council is chaired by the Premier and includes the
heads of each governmental department and agency. Currently, the council has 35
members: the premier, one executive vice premier, three vice premiers, ve state
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
238
239
councilors (of whom two are also ministers), and 25 additional ministers and chairs
of major agencies.
In 1952 the Jingjiang Flood Diversion Project was undertaken in the northeastern
part of the Gong’an County. Ocials at the Jingjiang Flood Diversion Management
Bureau were instructed to begin destroying dikes and opening oodgates when water
levels on the Yangtze reached a record high of 45 m (149 feet) at the monitoring
station in Shashi city, just north of the area that would be ooded. Fortunately, the
water level at the Shashi monitoring station remained approximately 6 cm (2 in.)
below the 45-m level. However, as a precautionary measure 330,000 people were
evacuated from the Jingjiang region.
August 6, 1998: Hubai Provincial Flood Control Headquarters advised that there
were critical ood levels. More than 300,000 people in the ood diversion area were
evacuated to make room for diverted oodwaters
August 16, 1998: The water level in Shashi rose to 45.22 meters, which exceeded the
45.00 meter state stipulated ood diversion mark. However, in a bid to reduce losses,
ocials decided not to divert water.
August 20, 1998: The sixth Yangtze River crest threatened Wuhan and 2.3 million
citizens and soldiers provided support for the Jingjiang River embankment.
However, in the summer of 1998 oods weakened the Jingjiang River embankment
so 40,000 Chinese People’s Liberation Army soldiers and half a million local citizens
helped to withstand the oodwaters.
3. YELLOW RIVER FLOOD WARFARE CASE STUDY
The Imperial Japanese Army quickly obtained large swaths of Chinese territory at
the onset of the Second Sino-Japanese War in 1937 and by June 1938, the Japanese
had control of all of North China. On June 6, 1938 the Japanese imperial army
captured Kaifeng, the capital of Henan, and threatened to take over Zhengzhou
which would have directly endangered the major Chinese cities of Wuhan and Xi’an
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
240
241
(as Zhengzhou stood at the junction of the critical Pinghan and Longhai Railways).
To deter further Japanese advances into western and southern China, the Chinese
Nationalist leader General Chiang Kai-shek opened the dikes on the Yellow River
near Zhengzhou on the advice of Chen Guofu, a prominent political ally in party
aairs. Specically, the dike was destroyed on June 5, 1938 and June 7, 1938 at
Huayuankou, on the south bank of the Yellow River causing ooding in the Eastern
states of Henan, Anhui, and Jiangsu.
The deliberate oods constitute what many consider to be the largest war induced
environmental disaster in history (Dutch, 2009; Lary, 2004). This act of environmental
warfare destroyed thousands of square kilometers of farmland and shifted the mouth
of the Yellow River hundreds of miles south. It is estimated that the disaster aected
approximately ve million people. In particular, the ood inundated thousands of
villages, thereby driving villagers from their homes and creating three million refugees.
It is estimated that at least 400,000-500,000 died from the ooding with another
half a million becoming homeless. Besides this large death toll, the ecological toll
on agricultural and other ecological resources was severe: crops in the abandoned,
ooded countryside were destroyed and irrigation channels were ruined. Moreover,
even once the water eventually receded the soil was often uncultivable as much of
the land was covered in silt. Both private property and public infrastructure were
destroyed, leaving survivors destitute.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
240
241
4. ANALYSIS TOOLS FOR UNDERSTANDING ETHICAL
AND SCIENTIFIC DIMENSIONS
Figure 1. Analysis tools for combing the science and ethics of disaster management.
A wide number of tools have been proposed for integrating the science and ethics
of disaster management, as shown in Figure 1 including group decision analysis and
soft computing approaches including fuzzy-neural analysis. Discriminant function
analysis (DFA) is typically employed in the presence of a categorical dependent
variable if all of the predictors are continuous and nicely distributed about the
mean. It is used primarily to predict group membership from a set of continuous
predictors. Specically, DFA assumes multivariate normality, i.e. the means of the
various Dependent Variables (DVs) in each cell and all linear combinations of the
DVs are normally distributed. On the other hand, Logit analysis is usually employed
if all the predictors are categorical.
Logistic regression is used to predict a categorical (usually dichotomous) variable from
a set of predictor variables where the predictor variables are a mix of continuous
and categorical variables and/or if they are not nicely distributed. The medical
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
242
243
community often uses logistic regression for problems in which the dependent
variable is whether or not a patient has a disease.
For a logistic regression, the predicted dependent variable is a function of the
probability that a particular individual will be in one of the categories (i.e. that an
individual has a disease, given her set of scores on the predictor variables).
As an example of the use of logistic regression in disaster research consider the
Yangtze River ood case study. Disaster management college students in India and
USA (N = 630) were asked to pretend that they were serving as a member of the
State Council of the People’s Republic of China hearing arguments for and against
the destruction of dikes at Jianli and Jingjiang. The discussion included a description
of the emergency management options in logical but emotional language. Various
points of view were put forth and computer simulations were carried out to show
the impact of the proposed evacuations and dam breaches on communities. Each
participant read one of ve dierent scenarios which described the goals and benets
of the proposed dam breaches. They were:
• ECONOMIC-protecting down-stream economic assets including the
central Chinese city of Wuhan and the major Beijing-Guangzhou Railway
transportation artery.
• ENVIRONMENTAL-the need to protect the productive Jianghan plain.
• SOCIAL-the challenges associated with evacuation and the threats to life
safety associated with ooding.
• POLITICAL-understanding the decision making process in China’s state
council and the Jingjiang Flood Diversion Management Bureau.
• MILITARY-camouaged soldiers reinforced the earthen dikes along the banks
of the Yangtze river in order to help back the swollen river. With waters on
the Yangtze at their highest levels in 44 years, nearly 5 million people in ve
provinces were mobilized to help fortify the embankments.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
242
243
After reading the background of the Yangtze River ooding and the other case
materials, each student was asked whether or not to proceed with the study.
Participating students were asked to ll out an Ethics Position Questionnaire (EPQ)
(Forsyth, 1980) which assesses personal moral philosophy. The instrument contains
two dimensions: relativism and idealism. Scoring high on the relativism subscale of
the EQP is consistent with a personal and situational moral philosophy and a rejection
of universal moral principles. Overall, high scorers on the idealism subscale of the
EQP espouse a concern for the welfare of others and believe that ethical behavior
leads only to positive outcomes, never to bad or mixed consequences (Forsyth, Nye,
& Kelley, 1985).
The EPQ asks questions about acceptance of issues that vary in terms of relativism
and idealism. The instrument contains 20 Likert-type items (each with a 9-point
response scale from “completely disagree” to “completely agree”). The relativism
scale includes items such as “Dierent types of moralities cannot be compared as to
‘rightness’” and “What is ethical varies from one situation to another.” The idealism
scale, in contrast, measures one’s perspective on positive and negative consequences
with such items as “A person should make certain that their actions never intentionally
harm another even to a small degree” and “If an action could harm an innocent
other than it should not be done” (Forsyth, 1980). It is important to note that idealists
(persons who score high on the idealism dimension) do not make ethical decisions
by balancing good consequences against negative outcomes; rather, for an idealist,
the existence of any negative outcomes may make a behavior unethical, even though
there may be many positive consequences.
Low Relativism
Absolutists: Principled idealists who
believe pwople should act in ways that
are consistent with moral rules, for doing
so will in most cases yield the best
outcomes for all concerned.
Situationists: Idealistic contextualists
who favor securing the best possible
consequences for all concerned even if
doing so will violate traditional rules that
dene what is right and what is wrong.
High
Idealism
High Relativism
Exceptionists: Principled pragmatists
who endorse moral rules as guides for
action, but admit that following rules
will not necessarily generate the best
consequences for all concerned.
Subjectivists: Pragmatic relativists who
base their ethical choices on personal
considerations, such as individualized
values, moral emotions, or an
idiosyncratic moral philosophy.
Low
Idealism
Figure 2. Four-fold classication of Personal Moralities based on Idealism and Relativism. Source:
(Forsyth, 1980).
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
244
245
A four-fold classication based on Idealism and Relativism is shown in Figure 2. There
are four quadrants that categorize various individuals on the basis of their personal
moral philosophy and ethical choices: Situationalists, Absolutists, Exceptionalists and
Subjectivists. Situationalists are highly relativistic and idealistic contextualists: they
believe that individuals should seek to achieve the best outcomes possible, but that
moral rules cannot be universally applied across all situations: adherents prescribe
close examination of individual situation in reaching a contextually appropriate
moral evaluation. Absolutists, like Situationists, are also idealistic; they support
decisions that yield positive, desirable consequences. However, Absolutists are not
relativistic; they believe that ethical absolutes must be included in any code of ethics.
Subjectivists and Exceptionalists are both low in terms of idealism. Subjectivists are
pragmatic relativists that reject universal moral rules (high relativism) and believe that
following rules will not necessarily lead to the best outcomes for all concerned (less
idealistic about the possibility of achieving humanitarian goals). Its adherents make
subjective, individualistic moral judgments rather than basing their ethical choices
on more “objective” information, such as universal moral absolutes or the extent to
which a given action harms others. Finally, Exceptionists are principled pragmatists
who endorse moral rules as guides for behavior but believe that following actions that
lead to some negative consequences shouldn’t necessarily be dismissed. Low in both
relativism and idealism they are willing to make exceptions to their moral principles.
Human and animal rights activists tend to be high in idealism and low in relativism.
This study examines whether gender, idealism and relativism are related towards
attitudes in complex emergency management decisions.
5. LOGISTIC REGRESSION ANALYSIS
We begin with a simple bivariate logistic regression, using student’s decisions as the
dichotomous criterion variable and gender as a dichotomous predictor variable
where we have coded gender with 0=Female, 1=Male and decision with 0=stop the
decision to breach the dam and 1=continue with the decision to breach the dam.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
244
245
Our regression model involves predicting the logit, i.e. the natural log of the odds of
have made one or the other decision. That is:
(1)
where
is the predicted probability of the event which is coded with 1 (continue
with the plans to destroy the dykes and open the oodgates) rather than 0 (not to
proceed with dynamiting the dykes and opening the oodgates).
is the predicted
probability of the other decision and X is the predictor variable, gender. The intercept
(constant term) is given by a and b is the slope from the logistic regression equation.
An iterative maximum likelihood procedure is used to construct a logistic regression
model. Starting with an arbitrary value of the regression coecients an initial
model for predicting the observed data is constructed. Errors in the aforementioned
prediction are then evaluated and the regression coecients are changed in order
to make the likelihood of the observed data greater under the new model. The
procedure is iterative since the procedure repeats until the dierences between the
latest model and the previous model are trivial (i.e. the model converges).
Observing the statistical output we observe that 630 cases are used in the analysis
(Figure 1).
Table 1. Case Processing Summary.
Case Processing Summary
Unweighted Cases
a
N Percent
Selected Cases
Included in Analysis 630 100.0
Missing Cases 0 .0
Total 630 100.0
Unselected Cases 0 .0
Total 630 100.0
Block 0 output is for a model that includes only the intercept (constant term). Given
the base rates of the two decision options 58.4% (i.e. 368/630) of students decided to
stop the dam destruction implementation while 41.6% decided to allow it to continue
(Table 2). Without any other information the best statistical inference is to predict,
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
246
247
for every case, that the student will decide to stop the dam destruction. Using that
strategy one would be correct 58.4% of the time.
Table 2. Classication Table.
Classication Table
a,b
Observed Predicted
Decision
Percentage Correct
Stop Continue
Step 0
Decision
Stop 368 0 100.0
Continue 262 0 .0
Overall Percentage 58.4
a. Constant is included in the model.
b. The cut value is .500
Table 3 (variables in the equation) shows that the intercept only model is:
which yields the predicted odds is [Exp(B)]=0.711. That is, the
predicted odds of deciding to continue with the dyke destruction is 0.711. Since 262
of the students decided to continue the dyke destruction and 368 decided to stop the
destruction, our observed odds are 262/368 = 0.712.
Table 3. Variables in the Equation.
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.340 .081 17.664 1 .000 .712
Now look at Block 1 output where the gender variable is added as a predictor. The
Omnibus Test of Model Coecients (Table 4) illustrates a Chi-square of 48.198 on 1
df, signicant beyond .001. This is a test of the null hypothesis that adding the gender
variable to the model has signicantly increased our ability to predict decisions made
by the students. The -2 Log likelihood statistic (807.247) models how well the model
predicts the decisions (the smaller the better) as shown in the model summary (Table
5). The Cox and Snell R
2
(0.074) is like the interpretation of R
2
in multiple regression,
but does not reach a maximum value of 1, whereas the Nagelkerke R
2
can reach a
maximum of 1.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
246
247
Table 4. Omnibus Tests of Model Coefcients.
Omnibus Tests of Model Coefcients
Chi-square df Sig.
Step 1
Step 48.198 1 .000
Block 48.198 1 .000
Model 48.198 1 .000
Table 5. Model Summary.
Model Summary
Step 1
-2 Log
likelihood
Cox & Snell R
Square
Nagelkerke R
Square
807.247
a
.074 .099
a. Estimation terminated at iteration number 3 because parameter estimates
changed by less than .001.
6. ANALYSIS
The Variables in the Equation output (Table 6) shows us that the regression
equation is
. We can now use this model
to predict the odds that a subject of a given gender will decide to continue
with the dyke destruction. When the student is a woman (gender = 0), then
. That is, a female student is only 0.455 as
likely to approve the dyke destruction as she is to stop the destruction. For male
students (gender = 1) then
. That is, a male
student is 1.474 times more likely to decide to continue the dyke destruction as she is
to stop the destruction.
The odds are now converted to probabilities. For women students in the study
That is the model predicts that 31% of women will decide to continue to destroy the
dykes. For men:
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
248
249
That is our model predicts that 60% of men will decide to continue to destroy the
dykes.
The variables in Equation output (Table 6) provides the odds ratio predicted by the
model: Exp(B) provided in the right most column. The odds ratio is obtained by
raising the base of the natural log to the b
th
power, where b is the slope from the
logistic regression equation. In our model the result is
which means
that the model predicts the odds of deciding to continue the dam destruction are
3.241 times higher for men than they are for women. For men, the odds are 1.474,
and for women they are 0.455. The odds ratio is: 1.474/0.455 = 3.24.
Table 6. Variables in the Equation.
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1
Gender 1.176 .172 46.570 1 .000 3.241
Constant -.788 .108 53.424 1 .000 .455
a. Variable(s) entered on step 1: gender.
In order to classify subjects according to their decision we establish the following
decision rule: If the probability of the event is greater than or equal to 0.5 (the
SPSS threshold set by default), then it is predicted that the event will take place (in
some cases we may wish to set the threshold higher or lower than 0.5). Using the 0.5
threshold we classify a student into the “Continue with Dam Destruction” category
if the estimated probability is more than 0.5, which it is for every male student. A
subject is classied into the “Stop the Dam Destruction” category if the estimated
probability is less than 0.5 which it is for every female student.
The sensitivity of the prediction, i.e. the percentage of occurrences correctly
predicted is now examined:
)(
occurredeventcorrectP
. For the “Continue with
Dam Destruction” event the Classication Table (Table 7) shows us that this rule
allows us to correctly classify 137/(125+137)=52.3% of the subjects where the
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.233-251
248
249
predicted event was observed. For the specicity of the prediction, i.e. the percentage
of nonoccurrence’s correctly predicted we have:
)( occurnotdideventcorrectP
= 275/ (275+93) = 74.7%. Hence this rule correctly classies 74.7% of the subjects
where the predicted event did not occur (i.e. “Stop Dam Destruction”). Overall the
predictions were correct 412 (i.e. 275+137) out of 630 times for an overall success
rate of 65.4%. Recall that the overall success rate was 58.4% for the model with the
intercept only.
Table 7. Classication Table.
Classication Table
a
Step 1
Observed Predicted
Decision
Stop Continue Percentage Correct
Stop 275 93 74.7
Continue 125 137 52.3
Overall Percentage 65.4
a. The cut value is .500
8. CONCLUSIONS
A wide number of tools are herein proposed for integrating the science and
ethics of disaster management. These include group decision analysis and soft
computing approaches including evolutionary algorithms and fuzzy-neural analyses.
Discriminant function analysis (DFA) is typically employed in the presence of a
categorical dependent variable if all of the predictors are continuous and uniformly
distributed about the mean. On the other hand, Logit analysis is usually employed
if all of the predictors are categorical It is shown that males are 1.474 times more
likely to decide to continue dyke destruction and sacrice human lives than females.
Ethical decisions are key to shaping the disaster studies eld and they must be
included in a formal disaster policy analysis: It is important that values and ethical
considerations are made explicit during the disaster planning process so that under
conditions of pressure of urgency and criticality they can be made consistent with the
ethical judgments that underlies the emergency management decision process. It is
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
250
251
concluded that in the community disaster planning phase, there will be innumerable
issues, each with its own ethical components. What humans and assets do we protect,
and to what level of safety? How do we set disaster budgets and priorities?
REFERENCES
Dutch, S. I. (2009). The Largest Act of Environmental Warfare in History. Environmental
& Engineering Geoscience, 15(4), 287-297. doi: https://doi.org/10.2113/
gseegeosci.15.4.287
Forsyth, D. R. (1980). A taxonomy of ethical ideologies. Journal of Personality and
Social psychology, 39(1), 175.
Forsyth, D. R., Nye, J. L., & Kelley, K. (1988). Idealism, relativism, and the ethic
of caring. The Journal of Psychology, 122(3), 243-248.
Kirby, A. (2001). Plan to tame Yangtze oods. Retrieved from: http://news.bbc.co.uk/2/
hi/science/nature/1595327.stm
Lary, D. (2004). The Waters Covered the Earth: China’s War-Induced Natural
Disaster. In Mark Selden and Alvin Y. So, ed. War and State Terrorism: The United
States, Japan, and the Asia-Pacic in the Long Twentieth Century. Rowman & Littleeld,
143-170.
Muscolino, M. S. (2014). The Ecology of War in China: Henan Province, the Yellow River,
and Beyond, 1938-1950. Cambridge University Press.
Samel, A. N., & Liang, X. Z. (2003). Understanding relationships between the
1998 Yangtze River ood and northeast Eurasian blocking. Climate Research, 23(2),
149-158.
