Knowledge-generation epistemology and the
foundations of engineering
Stephen Frezza, David Nordquest and Richard Moodey
frezza001@gannon.edu, nordques001@gannon.edu, and moodey001@gannon.edu
Abstract - This paper suggests that the purpose (goal) and
manner (method) of knowledge application and
generation usefully distinguishes engineering and
scientific knowledge. This method could be significantly
useful in distinguishing the scientific and engineering
components of engineering education, as well as
underscored the centrality of social context to
engineering work, and engineering values. This paper
presents a brief exploration of the epistemology of
knowledge, specifically distinguishing the development of
scientific knowledge from the development of
engineering knowledge. It outlines a pragmatic theory of
knowledge which provides a means by which to reliably
distinguish, particularly in a learning environment, the
critical terms of 'science', 'engineering science' and
'engineering.'
Index Terms – Philosophy of Engineering, Philosophy of
Engineering Education, Epistemology.
INTRODUCTION
A cursory introduction to the literature of the
philosophy of engineering reveals competing viewpoints on
what distinguishes scientific from engineering knowledge,
including engineering (and technology) as applied science
[2] and the influence of knowledge generation as a means to
distinguish between ‘scientific’ and ‘engineering’
knowledge. When seen through the lens of a pragmatic
theory of knowledge, the crucial characteristics of scientific
knowledge include that scientific knowledge is theory
bound, and scientific knowledge is developed to explain the
way the world works. [1] Engineering knowledge can be
considered a distinct form of knowledge since scientific and
engineering knowledge aim at different ends. In short,
science aims to explain and technology/engineering aims to
create artifices. "Technology, though it may apply science, is
not the same as or entirely applied science" [3].
Engineers are individuals who combine knowledge of
science, mathematics, and economics to solve technical
problems that confront society; “… a person who applies
science, mathematics, and economics to meet the needs of
humankind” [5]; alternatively defined, “Engineering is the
art of directing the great sources of power in nature for the
use and convenience of man” [6].
Individually, engineers are those who engage in the
practice of engineering. The root of the word engineer
derives from engine and ingenious, both of which come
978-1-4673-5261-1/13/$31.00 ©2013 IEEE
from the Latin root in generare, meaning “to create.” In
early English, the verb engine meant “to contrive” or “to
create.” The word engineer traces to around A.D. 200, when
the Christian author Tertullian described a Roman attack on
the Carthaginians using a battering ram described by him as
ingenium, an ingenious invention. Hence an engineer is a
person responsible for developing such ingenious engines
[of war] [5]. At their roots, engineers are problem solvers –
ingenious problem solvers. Engineers create products and
processes to improve and to enhance the convenience and
beauty of our everyday lives [7]. Thus, engineers are
civilizers, since civilization in its most elemental sense
means bringing humans physically out of the state of
primitive savagery [6].
Engineering knowledge is rooted in scientific
knowledge; however they appear to be separate spheres of
knowledge that are both man-made [8]. Historically, each
engineering sub-discipline has developed its own body of
knowledge, whether codified or not [9,10,11]. Among
engineering educators, it is commonly accepted that
engineering knowledge, however much based on the study
of mathematics, science and engineering science, is
insufficient. “…there is a big gap between scientific research
and the engineering product which has to be bridged by the
art of the engineer. The creative, constructive knowledge of
the engineer is the knowledge needed to implement that art.”
[3]
Beyond engineering knowledge and skill, there are the
behaviors that help engineers succeed, bring honor to their
profession, and make technology a force for good in the
world. Engineers are expected to link effectiveness and
creativity, where conscientiousness is the virtue suggested to
be the most valuable for engineers [6]. In sum, engineers
identify problems, construct solutions, and use their
knowledge and skill to change the status quo into a new
situation that is better, both individually and for mankind.
The types of knowledge produced, as well as the
processes by which knowledge assimilated and utilized are
different among these groups of persons. Central to these
sociological distinctions and their respective knowledge
generation/use is the relationship of different technological
disciplines to one another, and how the technological
disciplines effect knowledge generation.
SCIENCE AND ENGINEERING
‘Science’ and ‘Engineering’ are broad disciplines each with
long histories and significant nuances among them. The
viewpoints common to each discipline are however
different. The viewpoints of science and engineering are
idealistic and pragmatic respectively [12].
The nature, or goal of science as a whole, is to explain
how the world works, as determined through experiments
and their artifacts. Knowledge generation, from the
perspective of scientists is approached via creating a
hypothesis first, and testing it second. These scientific
findings and related technologies are often codified, and
managed by a body of scientists, and transmitted through
published works and education.
The nature of engineering is for addressing human
needs/problems, and generally applies domain-specific
heuristics for building a system, or components intended for
system deployment. [13] The lessons learned and identified
technologies are often (though not always) codified, and
may be transmitted informally, or formally to other
engineers in the form of best practices, or where applicable,
via a hypothesis that generalizes the best practice and testing
of that hypothesis. In either case, the more formalized
engineering findings are often codified, and managed by a
body of engineers, and transmitted through published works
and education.
Both science and engineering involve the identification
of and development of technology, and both include
significant relationship to and roots in mathematics. The
tested formalizations of theory, identified technology, and
best practices can lead to the creation of new branches of
engineering and/or science. While these processes share
significant similarities, their viewpoints make them
epistemologically different; the value driving scientific
knowledge development is that of explanation; the value
driving engineering knowledge generation is application.
These differences in value change the epistemological
character of the knowledge.
EPISTEMOLOGY AND KNOWLEDGE
Briefly stated epistemology is the study of knowledge as
justified belief. Development in epistemology since the work
of David Hume has focused on identifying the justified true
belief about what a single person knows. When applied to
disciplines or communities, it also includes the requirement
that to qualify as knowledge, a proposition or set of
propositions must be endorsed by an appropriate
community. Hence, there is both an individual and a
significant sociological aspect to knowledge and justified
belief. Individuals produce candidate claims for knowledge,
and these candidates become knowledge, once they are
endorsed by the appropriate community using agreed upon
standards. [1] Components of the method of the human mind
[14] and of Polanyi's Personal Knowledge [16] are outlined
as foundations for individual understanding, followed by an
outline centered on Polanyi, Sorokin and Vanderburg for the
sociological approaches to epistemology [17,18].
Individual and Communal Knowing
Lonergan argues for what we may term a “method of the
human mind,” consisting of experiencing, understanding,
and judging, underlies all specialized forms of knowing.
This basic heuristic method or structure is tailored in various
ways to meet the particular requirements of specialized
contexts such as everyday practical life and the fields of
mathematical and empirical science. [14]
In the "method of the human mind," experiencing
provides us with data and depends upon adherence to a norm
of attentiveness. Our striving to make sense of the data (e.g.,
“What is it?), promotes us from experiencing to attempts to
understand; to finding the form, pattern, meaning, or
significance of what we have experienced. Inquiry and
imagination yield insights, which are expressed in concepts
and definitions to provide a formulation of the understanding
we have attained. Inquiry, insight and formulation embody a
norm of intelligence. Because understandings may be
misunderstandings, we cannot stop with them but must go
on to ask the critical question, “is it really so?” The process
of answering this question thematizes our desire to move
through critical reflection to judgment. Judging marshals and
weighs the evidence to assess the adequacy of our
understanding. The evidence is adequate if it shows that the
conditions necessary for something's being so are all met. If
the conditions are met, within this context our knowing
reaches a “virtually unconditioned” state, whose conditions
for justified belief are all fulfilled. Thus the understanding is
no longer is conditional and until proven otherwise, rather it
must be true. The norm embodied in these operations of
judging is that of reasonableness. The overall method is
adjusted based on the perceived need for timeliness,
precision,
comprehensiveness,
universality,
and/or
completeness. [14] This is where the viewpoint of
knowledge is critical, as individual need or use for the
proposition and the social context affect the manner in which
something is known.
Thus knowing is a process, involving both covert and
overt human actions, which involve the application of
personal and social criteria. These knowledge-generation
actions apply to the production of scientific and
technological things. Polanyi describes covert actions to
include operations such as sensing, perceiving, imagining,
understanding, judging, and deciding. The overt actions
include operations such as speaking, writing, drawing,
calculating, grasping, shaping materials, and using tools.
[16]
Individual knowing is a precursor to the community
endorsement necessary for knowledge and justified belief to
become part of the body of accepted knowledge. Both covert
and overt actions have an internal structure that Polanyi calls
a from-to relation. A skillful achievement, whether practical
or theoretical, is the to- term of this relation, and the
subordinated particulars constitute the from term. He
suggests a movement metaphor for this relation when, in
discussing the from-to relation in acts of knowing. [16]
Sociological models of the epistemological process by which
we create scientific and technological products also embody
a movement metaphor. As presented in Table 1, Sorokin
breaks the process into three stages, which are used here as
an outline of the individual + communal knowledge
development process. [17] For technical development,
Vanderburg proposes a “technological cycle” with five
phases. [18] In both models, the movements are from each
earlier stage or phase to the next one.
Table 1. Correspondences between knowledge
development stages [17], and phases of technological
development [18]
Sorokin (3 Stage) Model
1. Mental Integration
2. Empirical
Objectification
3. Socialization
Vanderburg (5 Phase)
Technological Cycle
1. Invention
2. Innovation and
Development
3. Application
4. Diffusion
5. Displacement
This movement parallels Lonergan’s proposals.
According to Morelli and Morelli, Lonergan similarly
“identifies the scheme of recurrence which constitutes
technological, economic, and political advance: situation –
insight – communication – persuasion – agreement –
decision – action – new situation – insight,” etc.[15]
Mental Integration: In defining “mental integration,”
Sorokin states that the “integration of two or more meanings
into one system is an act of creation occurring in the human
mind.” [17] This treats it as a covert act “in the human
mind.” Vanderburg’s description of “invention” includes
both covert acts, covert states, and overt acts [18], although
the acts of exploring and working out details are usually
overt actions, including actions such as writing, calculating,
sketching, building physical models, and conversing with
others.
Empirical Objectification: Sorokin’s characterization of
empirical objectification emphasizes the need for "empirical
vehicles through which [new knowledge propositions] can
be conveyed to others.” [17] These can be incarnated
through documents, examples, products, or other means.
They key point among these processes is the development
and presentation to a wider (more than individual) audience.
Socialization implies that the “empirical vehicles” are
utilized to share knowledge among individuals, led by agents
of socialization. He adds that most of the thousands of texts
and artifacts produced never succeed in socialization, in
being accepted and used by anybody but their authors. [17].
The key here is that people are the agents of socialization,
and socialization efforts begin early in the process and
continue throughout.
When the original inventor or
discoverer attempts to explain his idea to another person, he
is an agent of socialization. When members of an original
group attempt to persuade controllers of resources to support
their project, market products, etc., they are agents of
socialization.
The point of these phases is to recognize that
individuals produce candidate claims for knowledge, and
these candidates become knowledge once they are endorsed
by the appropriate community using agreed upon standards.
[19] The importance of the different stages is two-fold: first
to recognize the importance of the inner mental state of a
single individual, and to understand the difficulties this
presents with respect to the certainty with which one can
assert that someone actually ‘knows’ something. Among
philosophers, this has led to “devising doomed criteria by
which we can determine whether an individual uttering a
proposition with X, Y, and Z properties can be said to
‘know’ something. The criteria are doomed because they
ignore contingency, historical and otherwise.” [1]
A pragmatic view of knowledge, on the other hand,
shifts the emphasis to the criteria that the community has
devised. But, even here, the criteria must meet some bottom
line condition. For the pragmatist the bottom line is
successful action. C. I. Lewis put it this way: "the utility of
knowledge lies in the control it gives us, through appropriate
action, over the quality of our future experience" [4]. This
provides a key to understanding scientific knowledge,
particularly in distinguishing it from other types of
knowledge.
UNDERSTANDING SCIENTIFIC KNOWLEDGE
Moving from these more generalized forms of knowledgegeneration to study those used in engineering and science is
not straightforward. Common sense knowing is a form of the
general method of the mind [14] where the process is
tailored to the requirement for timely action and has less
need for great precision, comprehensiveness, universality, or
completeness, especially where attempting to meet such
standards would only delay action unnecessarily. Common
sense is content to find out only what we need to know right
now, in this particular context, and to formulate itself
allusively and incompletely in rules of thumb, examples,
proverbs, and parables. Because its concern is our practical
living, it relates things to ourselves rather than to each other,
as the mathematical equations of science would do. In this
sense, scientific knowing (sciences) develops from common
sense and may be more or less distant from it. This distance
is critical, and plays a difficult role in the development of a
‘new’ science. For example, botany, relying on descriptions
of plants as they appear to us, is much closer to a common
sense method than the current study of plant genetics. Yet,
botany is a well-recognized subfield within biology, and
both common sense and theoretical approaches are used in
its pedagogy.
Lonergan distinguishes mathematical and empirical
scientific heuristic structures. He subdivides empirical
approaches into classical and statistical types. Classical
approaches yield an intrinsic intelligibility and pertain to
systematic aspects of reality, marked by schemes of
recurrence. Statistical approaches pertain to non-systematic
aspects of reality and yield no intrinsic intelligibility but,
rather, probabilities, ideal frequencies around which actual
events tend to cluster randomly.
The central point is that scientific claims derive their
meaning from the theories within which they are associated;
hence, scientific knowledge is theory-bound. The dynamic
process in which scientists continuously revise what they are
willing to endorse – and by which they examine their
assumptions and their methods – is at the very heart of the
strength of the sciences. This is a dynamic process that
develops ever more refined approximations of objective
truth, with both the challenge that it points to objective truth,
and at the same time constantly criticizing the approximation
as it is currently known. Thus, despite the theory-bound
nature of scientific knowledge, the self-critical process of
scientific inquiry ensures that the knowledge it claims is the
best available at that time insofar as it is judged "best"
according to community standards. The ultimate aim of
scientific inquiry is explanation. Thus, in the context of a
pragmatic account, the ultimate success of the use of
scientific knowledge is explanation. [1]
UNDERSTANDING ENGINEERING KNOWLEDGE
‘–Š•…‹‡…‡ƒ†technologyƒ›„‘””‘™ˆ”‘‘””‡Ž›‘
‡ƒ…Š ‘–Š‡” ‹ ˜ƒ”‹‘—• ™ƒ›• Ȃ –Š‡› constitute two distinct
forms of knowledge since they have distinguishably
different ends. Science aims to explain and
technology/engineering aims to create artifacts. Vincenti
puts it this way, "technology, though it may apply science, is
not the same as or entirely applied science" [3]. This is
important, as the explanatory value of a scientific theory
(e.g., its application) is subsequently judged by its usefulness
not in accurately estimating the objective truth that may be
involved, but rather for a different end: how the application
of the scientific theory helps in the creation of an artifact that
meets some human need.
Engineering refers to the practice of organizing the
design and construction and operation of any artifice which
transforms the physical or social world around us to meet
some recognized need. Engineering – like science – is an
activity with specific objectives. Consequently engineering
knowledge concerns the design, construction, and operation
of artifices for the purpose of manipulating the human
environment. [1] One can reasonably narrow the focus of
engineering knowledge to the topic of "design knowledge,"
by concentrating on design.
"Design" in this context denotes both the content of a
set of plans (as in "the design for a new airplane") and the
process by which those plans are produced. In the latter
meaning, it typically involves tentative layout (or layouts) of
the arrangement and dimensions of the artifice, checking of
the candidate device by mathematical analysis or
experimental test to see if it does the required job, and
modification when (as commonly happens at first) it does
not. This is a process of refinement, subject to a perceived
need for timeliness, precision, comprehensiveness, and/or
completeness. As such, this procedure usually requires
several
iterations
before
finally
dimensioned
plans/specifications/requirements can be released for
production, and the effort is judged by its results – the
usefulness of the design and/or product [1] [3]. This
refinement process has been reasonably characterized as
heuristic, and the development of such heuristics is central to
the engineering method [13].
‘Usefulness’ is a fundamentally subjective value; it is
necessarily a social construction that depends upon the
social and historical situations in which the problem and
those solving the problem exist. This is a challenge in
engineering and to engineering education in particular, as the
study of historical and social values and value construction
is typically lacking in engineering curricula [6].
The concept referenced here is often referred to as
‘subjective truth’, that is a truth by agreement, an agreement
of persons. This shows up in the engineering method as the
value of the engineering artifact/effort is assessed by specific
subjects (e.g., stakeholders); the requirement is there
because the users want it, etc. In short, the ‘who’ of the
problem is essential to solving the problem. In specific
engineering sub-disciplines such as requirements
engineering, incorrect assessment of the ‘who’ (subject) of
the problem is considered a major process failure. Gause and
Weinberg put it this way: “People have problems, systems
have requirements” [20]. Thus, understanding and applying
the ‘pragmatic’ approach in engineering is assessed by
subjects for whom the engineering method is applied. The
subject, and thus subjective truth remains central to
engineering. This is another area where scientific knowledge
differs significantly from engineering knowledge.
INTEGRATING SCIENTIFIC AND ENGINEERING KNOWLEDGE
Comparing the knowledge generation processes involved,
engineering and science appear related, but significantly
different, differentiated in both purpose and manner. This
follows the distinctions suggested by Lonergan in his
observations on empirical science. One of the distinctions
between mathematical and empirical science/research is the
empirical researcher's return to overlooked or neglected data
that “forces the revision of initial viewpoints.”
“The circuit, then, of mathematical development may be
named immanent; [as] it moves from images through
insights and conceptions to the production of symbolic
images whence higher insights arise. But the circuit of
[empirical] scientific development includes action upon
external things; it moves from observation and
experiment to tabulations and graphs, from these to
insights and formulations, from formulations to
forecasts, from forecasts to operations, in which it
obtains fresh evidence either for confirmation or for the
revision of existing views." [14]
One of the key pragmatic distinctions between this
iterative scientific approach and the engineering approach is
its goal: The scientist aims at explanation: universal,
reliable, comprehensive and sufficiently precise formulation
of knowledge; the engineer aims at timeliness, completeness,
with sufficient precision and comprehension. These values
affect the knowledge generation, retention and use.
Pitt provides a similar comparison of scientific and
engineering knowledge:
“First, the characterization of scientific knowledge as
theory-bound and aiming at explanation appears to be in
sharp contrast to the …task-specific knowledge of
engineering that aims at the production of an artifact to
serve a predetermined purpose.
“The second important difference between the two
forms of knowledge [is]… the manner in which
engineers solve their problems does have a distinctive
aspect. The solution to specific kinds of problems ends
up cataloged and recorded in the form of reference
works which can be employed across engineering areas.
For example, measuring material stress has been
systematized to a great extent. Depending on the
material, how to do it can be found in an appropriate
book. This gives rise to the idea that much engineering
is "cookbook engineering," but what is forgotten in this
caricature is that another part of the necessary
knowledge is knowing what book to look for. This a
unique form of knowledge that engineers bring to
problem solving. [1]
The viewpoints of science and engineering are
idealistic and pragmatic respectively [12]. The nature of
science explains how the world works and this can be
learned though experiments (producing artifacts).
Knowledge generation, from the perspective of scientists is
approached via creating a hypothesis first, and testing it
second. The nature of engineering is for addressing human
needs/problems, and generally applies domain-specific
heuristics for building a system. What distinguishes
scientific knowledge from engineering knowledge is not the
creation or use of artifacts/technology, rather it is the values
stemming from the scientific and engineering viewpoints
and how they are pragmatically applied (purpose and
method). This distinction in knowledge generation is both
individual and social; it affects the use of the knowledge,
and shapes the manner in which it is generated.
APPLYING A PRAGMATIC THEORY OF KNOWLEDGE
The pragmatic theory of knowledge, at least as outlined here
identifies purpose (goal) and manner (method) of knowledge
application and generation as distinguishable characteristics
that can be used to distinguish engineering foundations.
Historically the 19th Century ‘French’ (Science and Math)
and ‘English’ (Apprenticeship) schools of engineering have
significantly influenced the training and values of current
engineering training [6]. The science and math school
emphasizes knowledge and knowledge generation whereas
the apprenticeship school emphasizes pragmatic application.
The difficulty lies in trying to integrate these schools, when
the science and math approach de-emphasizes the pragmatic,
and the apprenticeship approach de-emphasizes knowledge
generation. The foundations of engineering, as argued in this
paper, necessarily include both pragmatic knowledge and
pragmatic knowledge generation. Furthermore, the
knowledge-generation
epistemology
implies
that
engineering foundations are more than just the knowledge of
science and mathematics, more than applied math and
science, and more than pragmatic apprenticeship of an
engineering design method. Engineering, at its foundations
is ‘both’ using science ‘and’ pragmatic, and more than both:
it is about generating and using knowledge for a purpose and
with a method that is more than theory, more than
descriptive: it is useful.
This ‘both and’ view that emphasizes both goal and
method of engineering can be very useful in trying to help
distinguish a particular engineering discipline from its
related foundations in math and science (natural or
otherwise). Similarly it can be used to help distinguish
emerging engineering disciplines from their respective math
and sciences (e.g., see [21]).
PRACTICAL IMPLICATIONS
This epistemological work also suggests that there is a
significantly engineering philosophy that should be reflected
in engineering education. This implies that an engineering
approach is more than engineering knowledge – rather, that
it is both knowledge and approach. The implications of
applying this epistemological lens to the current practice of
engineering education could be significant in several areas
such as accreditation/program development, defining traits
for engineering education, and for exploring affective
domain learning.
This epistemological lens would be particularly useful
in developing both a consistent definition for, and
mechanism for distinguishing the ‘scientific’ and
‘engineering’ components of accredited engineering degree
programs, such as those accredited by ABET and other
national- and international boards [21]. Using this or a
similar epistemological lens would allow for more consistent
discussion, communication, and training for accreditors and
program developers. This would be particularly useful for
delineating the scientific roots and contributions of
engineering sciences (e.g. Thermal Science), or sciences
with significantly shorter histories than most natural sciences
(e.g., Computer Science) [22].
Another area where this epistemological lens could be
well applied is in helping undergraduate engineers
reasonably frame and value what they actually do, Lonergan
calls this self-appropriation and argues that if you
"[t]horoughly understand what it is to understand ,. . . not
only will you understand the broad lines of all there is to be
understood but also you will possess a fixed base, an
invariant pattern, opening upon all further developments of
understanding." [14]
Knowing more explicitly what it is to know and to solve
problems in engineering will give undergraduate engineers
fuller anticipations of the paths to solutions. They may come
to appreciate more fully how the value of their work, of an
engineering solution, lies in a timely, sufficiently complete,
sufficiently precise solution that is in fact delivered. This
shifts the emphasis onto understanding sufficiency, involves
understanding and applying the social context of their work,
and away from single-solution thinking, away from ‘analysis
paralysis’ where the ‘best is the enemy of the good enough.’
An example of this it is not the one single, best right answer,
that comprises good engineering. Rather the best
engineering answer(s) are judged pragmatically, and
routinely involve social context (e.g. a company, a
customer). While this emphasis appears in common to
engineering curricula (e.g. ABET), this work suggests that
this is not a facet of engineering education, or a ‘nice to
have’ aspect, but rather is intrinsically necessary to the
foundations of engineering.
A third area where this approach could be useful for
engineering educators lies in helping to shape discussions
surrounding the affective domain of engineering. Currently,
the authors’ expectations are that essentially all engineering
program-level outcomes, and probably course-level
outcomes are routinely voiced in terms of cognitive domain
outcomes. This, and related works (e.g., [1,3,6,13]) suggest
that the engineering community has significant, if not
universally communicated expectations for the affective
domain of engineering graduates. While these philosophical
perspectives provide a framework for such explorations,
they strongly imply that such work in using these
epistemological lenses would both inform and help guide
exploration of affective domain expectations for engineers
and engineering programs. This area remains to be
significantly explored.
CONCLUSION
The pragmatic theory of knowledge, briefly presented, uses
the purpose (goal) and manner (method) of knowledge
application and generation to distinguish types of
knowledge: engineering and scientific. The pragmatic theory
of knowledge provides a means by which, particularly in a
learning environment, the critical terms of ‘science’, and
‘engineering’ can be reliably distinguished.
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
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Proceedings of the 120th ASEE Annual Conference and
Exposition, Atlanta, GA, June 23-26, 2013.
ACKNOWLEDGEMENT
The authors would like to gratefully acknowledge the
assistance of our graduate assistant, Mr. Krishnakishore Pilla
in the development of original developments of this work.
AUTHOR INFORMATION
Stephen T. Frezza, C.S.D.P. Professor of Software
Engineering, Computer and Information Science
Department, Gannon University, frezza001@gannon.edu
David A. Nordquest, Assistant Professor of Philosophy,
Philosophy Department, Gannon University,
nordques001@gannon.edu
Richard W. Moodey, Assistant Professor of Sociology,
Sociology Department, Gannon University,
Moodey001@gannon.edu