Scientific Research and Social Processes

All aspects of scientific research relate, in some manner, to social processes and are subject to the constraints of law and civil behavior that we expect from any public or private undertaking. Scientific research comprises more than just studies within a lab, as it can also describe advances in engineering, technical and computational developments, applying science to meet public needs, using technical information to guide policy, and other similar areas where a scientific approach is being used to address needs for new knowledge and insight into problems and curiosities.

Figure 1.3: Laboratory Test Tubes

Credit: Chokniti Khongchum licensed under CC0

The production of scientific research is tied to politics, social needs, public funding, venture capital, human health, environmental security, and economic development, as well as many other concerns of human society. As such, scientific research itself is subject to many forces and constraints working it, constraints which shape research questions, methods, and outcomes. Understanding and determining appropriate responses to many of these constraints requires a broad understanding of research ethics.

All scientific research is subject to social forces, therefore all research necessitates the consideration of ethics.

Research Ethics

Research ethics, thus: are a matter of responsible professional conduct fitting to the norms of a research community (procedural ethics); require a consideration of the broader social, political, and economic impacts (extrinsic ethics); and, point to where (social, personal, institutional) values and preferences become embedded in the analytical inputs and outputs of research itself (intrinsic ethics). A comprehensive consideration of research ethics requires a critical analysis of the procedural, extrinsic, and intrinsic aspects of the research or outputs under consideration. Goals for learning ethics include the identification and application of ethical tools for prescribing optimal solutions, the development of moral literacy, awareness of stakeholders, and the minimization of risk.

Figure 1.4: Search of ethics violations, Click on image to get more recent ethics violations

Credit: Google

Making Good Choices

Understanding how to make good choices as practitioners and leaders in the fields of renewables and sustainability will require both scientific knowledge and an awareness of the various positions along with projected trade-offs. These types of analyses require the consideration of more than technological optimization or basic costs and benefits; as numerous cases demonstrate, they often require the deeper consideration of ethical issues and embedded values. Not understanding these ethical issues and embedded values in the production of research and professional application of training can lead to outcomes that are unjust, increase risk, change economic relationships.

Not paying attention to ethical norms and proper research conduct can impact careers.

Impacts on Career

Careers can be directly impacted by ethical violations. Tenured jobs are lost over research ethics violations; foreign nationals can be deported over non-compliance when researching on government funds; entire labs have been closed due to ethics violations.

Ethical Comprehension is Not Easy

Ethics can be tricky, particularly when a practitioner researcher may be representing both personal interests and organizational interests in the same role (such as a reviewer of grant applications). It is not always obvious what is right and wrong behavior in certain situations, such as in considering conflicts of interest, or whether one can remove bias in reviewing the work of a friend or the work of someone from an opposing viewpoint. The key is to learn about ethics and where to go to learn more–find someone you can talk with about the issues at hand.

Ethics of Systems Research

Figure 1.5: EDSR Venn Diagram

Credit: Erich W. Schienke © Penn State University is licensed under CC BY-NC-SA 4.0

The Ethical Dimensions of Scientific/Systems Research (EDSR) approach describes how to recognize and evaluate ethical issues in research procedure and conduct, in the consideration of broader public and environmental impacts, and as values become embedded in research and analysis itself. Because common topics, types, and methods for ethical recognition and analysis are common across many cases of scientific research and technical application, it is efficient and helpful to develop a set of tools for critical reflection on various issues of ethical importance.

The EDSR Approach

As developed in the EDSR approach, three main categorical distinctions for research ethics used here are 1) broader social and political impacts of research (extrinsic ethics), 2) research practice and conduct (procedural ethics), and 3) embedded values within research (intrinsic ethics). By showing where and how to look for these types of ethical issues, the EDSR approach helps practitioners to anticipate where ethical issues may arise in a given research or application context.

Ethics Requires Comprehension and Critical Thinking

Research ethics is not a matter of memorization of rules about proper behavior. Rather, it is important to approach learning research ethics as the skill of being able to derive the ethics of a given situation, by asking similar key questions across multiple situations. While ethical contexts and possibilities are vast for a field like sustainability or renewable energy, we can still maintain a reasonable handle on things by addressing some core principles.

Figure 1.7: Person Holding Laboratory Flask

Credit: Chokniti Khongchum licensed under CC0

Basic Research Misconduct

Known as the three “cardinal sins” of research conduct, falsification, fabrication, and plagiarism (FFP) are the primary concerns in avoiding research misconduct. Any divergence from these norms undermines the integrity of research for that individual, lab, university/corporation, and the field as a whole.

Falsification

Falsification is the changing or omission of research results (data) to support claims, hypotheses, other data, etc. Falsification can include the manipulation of research instrumentation, materials, or processes. Manipulation of images or representations in a manner that distorts the data or “reads too much between the lines” can also be considered falsification.

Fabrication

Fabrication is the construction and/or addition of data, observations, or characterizations that never occurred in the gathering of data or running of experiments. Fabrication can occur when “filling out” the rest of experiment runs, for example. Claims about results need to be made on complete data sets (as is normally assumed), where claims made based on incomplete or assumed results is a form of fabrication.

Plagiarism

Plagiarism is, perhaps, the most common form of research misconduct. Researchers must be aware to cite all sources and take careful notes. Using or representing the work of others as your own work constitutes plagiarism, even if committed unintentionally. When reviewing privileged information, such as when reviewing grants or journal article manuscripts for peer review, researchers must recognize that what they are reading cannot be used for their own purposes because it cannot be cited until the work is published or publicly available.

“Cases of misconduct in science involving fabrication, falsification, and plagiarism breach the trust that allows scientists to build on others’ work, as well as eroding the trust that allows policymakers and others to make decisions based on scientific and objective evidence. The inability or refusal of research institutions to address such cases can undermine both the integrity of the research process and self-governance by the research community.”
 

Multiple Interests

A conflict of interest arises when one’s judgment is compromised based on connections, favors, or competing interests, and/or when one’s position is used to gain favor or extra rewards. Conflicts of interest are not always immediately obvious, nor does a conflict of interest in-and-of-itself constitute wrongdoing.

Multiple Conflicts

Personal obligations, connections to other institutions, participation in other research programs, or drawing from competing pools of funding can influence one’s capacity to be impartial in a given situation. Being impartial is as necessary in producing and reviewing scientific research as it is in jury selection in a court of law or in the practice of medicine. Perfect impartiality is not really possible, as we are always assessing a situation based on the unique culmination of our experiences and perspectives. Nevertheless, there are experiences, perspectives, and connections that may cause us to not be able to think outside of our own interests. Knowing when we are or are not able to think outside of our other interests is crucial to understanding how to avoid possible conflicts of interest. It is important to note that having an opposing viewpoint does not constitute a conflict of interest and is a cornerstone to robust reviews.

“Authors should also realize that disclosing financial support does not automatically diminish the credibility of the research. However, failure to disclosed a competing financial interest that is subsequently discovered immediately opens the authors to questions about objectivity.”

Corrosions to Impartiality

Problems that can erode impartiality in a given analysis should be explicitly stated and made transparent, often arising when different sources of resources are being invested in research. Using public funds for research in support of research for a private company can also be problematic. Conflicts of interest can also skew one’s perspective towards seeing or interpreting results that may not be there, or in ignoring data that are there. For example, conflicts can arise when companies are determining the health risks their products may pose, such as the risks of smoking being tested by tobacco companies.

The key to avoiding possible conflicts of interest is transparency of plausible interest in a given situation. Reveal all relevant connections to the case at hand. Recuse oneself from the case at hand if necessary.

Data are Fundamental to Research

Data are the core of research. The recent requirements by federally funded grants to develop data management plans summarize the imperatives here, including long-term storage of data, sharing of data, and other aspects of assuring data integrity, continuity, and federation. Data is considered part of the investment into research, in that it should be accessible to future researchers. Further, data or samples may be subject to other forms of analysis in the future, thus the future potential for data should also be taken into consideration when implementing management plans. As well, data security and privacy of subject data is of key importance to the protection of research subjects.

Interoperability

Interoperability of data, particularly across research institutions, is crucial in conducting collaborative research across a large network, such as in large scale public health networks. Paying attention and adhering to meta-data standards (information about data types and data structures) is of growing importance in sharing data between research communities, across disciplines, between regulatory institutions, governmental offices, and NGOs.

Data Standards and Storage

Attention to research data standards is crucial to avoiding cases such as when the thrust of the Mars Climate Orbiter was using metric unit Newtons (N) while the NASA ground crew was using the Imperial measure Pound-force (lbf), a mistake which caused the subsequent loss of the $500 million (US) satellite.

National Science Foundation (NSF) Data Sharing Policy

Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections, and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing.

Identification of Authorship

The identification of authors, the ordering of authors, the speed of publication of research findings, modes of research dissemination, acknowledgments, relevancy, and other aspects of publishing and disseminating findings. Proper citations are the foremost responsibility of authorship in the sciences. It is extremely important to adequately and accurately cite literature to give credit to those who have conducted research before you. It is better to be cautious and cite when unsure to avoid even the appearance of plagiarism.

Credit where Credit is Due

Authorship credit should go to anyone providing a substantial intellectual contribution to the paper. Disciplines have a variety of traditions in who should be counted as an author. This is also the case for the order of authorship, particularly who gets to be listed as the first and last author, as many labs and/or fields have their own best practices for listing authors. This is a conversation worth having with an advisor at some point during graduate training. Provide an acknowledgment for those individuals and organizations that provided advice, revision suggestions, material resources, and funding.

Discuss Authorship Upfront

It is worth discussing authorship at the beginning of a project to avoid conflicting expectations when it comes time to publish. All authors must be ready to defend the integrity of the research and the findings presented within. On multi-authored papers, individuals are responsible for their contributions.

“Authorship and collaboration problems are a serious threat to the research enterprise and to the motivation of young scientists, especially when they involve misappropriation of ideas and data.”

Responsible Publishing

Responsible authorship also must consider membership within a research community. Avoid fragmentary publications, where research findings can be presented in a comprehensive format, i.e., publishing fewer results per paper to increase the number of personal publications. Further, avoid simultaneous manuscript submissions to multiple journals. (Most journals have policies against simultaneous submissions.) Publish substantial findings, first and foremost, in a timely fashion. As well, be fair in the peer review process.

Research Impacts Policy

Scientific research can and often does impact public policy in a manner of ways. Understanding that one’s research may be applicable to informing public policy decisions or be subject to regulatory mechanisms is crucial. There are many three main intersections between policy and research that need to be considered, such as policy and regulation about the scientific research and/or technology (policy of science, or science policy); scientific research and technological capacity often informs crucial decision-making processes, such as determination of risks and evaluation of responses (science for policy); and, institutional policies in support of funding and conducting research (research management policy).

Regulatory Implication

Energy and Environment Systems present some significantly challenging scenarios for current and future generations. Further, this type of research is often used to direct regulatory policies, such as in the choice of national sustainable energy strategies and analysis of contingencies, etc.

Figure 1.9: People in Meeting

Credit: Adobe Stock licensed under CC0

Application Implications

Energy and environmental systems need to be co-guided to assure public and environmental safety as well as effective production in meeting demands. How, where, and when energy systems research will be applied will often come under the consideration of public officials and agency specialists.

"Science is organized knowledge. Wisdom is organized life."

Research for Decision-making

Scientific research is often put to use in decision-making processes. Further, science often informs society about risks that need to be avoided. Of course, much debate can arise from what to do about this new knowledge, such as has often been the case with climate change. Analyses, information, data, expert opinion, reports to congressional commissions, models, projections, solutions, new directions for economic development, etc., all require considering implications.

Property Rights

The coupled and interconnected nature of energy and environment systems will present many unique legal challenges, particularly where regulatory issues cross paths with land use changes, intellectual property rights, licensing agreements, public investments, commercialization, international trade, and distribution. Some of these concerns will also be covered by wider policies and regulation of energy markets, assessment of environmental impacts, and institution specific requirements.

Global Increase in Patents

New patents in energy are being filed globally on a daily basis, establishing a rapidly changing legal framework around ownership of and access to new energy technologies. The total (global) patent filings in alternative energy alone, "have increased at a rate of 10 percent per year starting in the 1990s and at a rate of 25 percent from 2001." (World Intellectual Property Organization, 2009) Questions also arise when considering how to license these technologies depending on location and development conditions. The rate of filing new energy related patents is projected to continue increasing over the next two decades, presenting significant opportunities and many uncertainties.

Public and Private Properties

Energy systems are quite diverse and can have a wide range of impacts on private and public property. Biofuels present significant opportunities for a low-carbon impact production of energy, but they also will likely change how we manage forests, crops, and other large-scale feedstock production. Wind energy technologies, while promising, will continue to pose oppositions to their locations, such as impacts on property values, visual preferences, etc. Regardless of the specific technology, innovation, adoption, and licensing of energy technologies will inevitably require further nuance and distinction, often based along ethical considerations.

Figure 1.10: Bitcoins and U.S. Dollar Bills

Credit: David McBee licensed under CC0

Changes in Economic Production

Energy and environment science and technology present possibilities that could potentially transform the shape of economic production, output, market arrangements, etc. For example, if developments in renewable energy can begin to produce long-lasting and economically feasible means for producing reliable energy at significantly reduced price, competitive advantage will typically drive producers towards adoption of new energy production techniques, which could have broad-reaching implications for economic conditions globally.

Daily Functions

It is crucial to ask whether the research could impact how society functions on a day-to-day basis; for example, how we grow food, produce energy, etc. Energy innovations will certainly have sweeping impacts across many aspects of society, aspects and issues which need to be contemplated in the formulation of research and design trajectories, and not just after the fact of invention.

Public Understanding

The public understanding of energy and environment systems presents significant challenges, particularly in trying to communicate risks, challenges, etc. Further, rising to the challenge of a prepared “sustainable energy” workforce is very much a concern of K-Graduate education.

Social Production

Transformations in energy and environment systems will inevitably present challenging questions about economic growth, social welfare, and public goods, the education of both future energy and environment scientists, increases in public understanding of energy systems, etc. The full arrival of sustainable energy based manufacturing will also have profound effects on traditional modes of fabrication and production.

Figure 1.11: Protestors

Credit: Stephanie Lanzetti licensed under CC0

The Common Good

Most people would tend to agree with the stance that our developments in science and technology should adhere to, or at least not be entirely counter to, our notions of the common good, not harming others, not causing further hardships, etc. After all, most people view science and technology as a positive force in society. However, this cannot always be assumed. Further, how we go about making sure society actually does benefit from innovations and new knowledge is not always straightforward, particularly in considering cutting edge research. There are three basic areas worthy of deeper analysis when considering the broader impacts of a given development trajectory.

Distributive Justice (equity)

Are the costs, harms, and benefits of nanotechnologies being distributed equitably over society? Can energy technology be used to improve the least well off first? Are certain populations more at risk from energy production than others (children, poor, elderly)?

Procedural Justice (due process)

How are decisions about energy and environment regulation being taken into account, and who is making the decisions and choices? If groups or individuals are going to be impacted by the development and application of certain energy technologies (i.e., stakeholders), are they included in the decision-making process? What sort of representation and proof of risk must an organization provide before moving forward with a new product or process?

Intergenerational justice (long-term)

Choices made now about infrastructure, investments, longevity, and risk can have implications for generations to come. For example, once the decision was made to develop nuclear technology, a choice was also made for many, many generations to follow. Infrastructure that is developed also needs to be maintained, or allowed to go to waste. All of these imply costs and opportunities (gained and lost) for decades, centuries, and in some cases, millennia.

Three main social justice concerns

• equitable distribution of benefits and harms
• fair and representative decision-making processes
• consideration of the needs of future generations

Emerging Risks

Approaching any new territory in science and technology can present great payoffs and public goods, but it can also present daunting challenges that can change and shape international relations. For example, nuclear science and technology continue to present similar challenges to governments and populations across the world. Once certain knowledge or technology is produced, published, circulated, or otherwise manifested into the world, it cannot be undone.

Assessing Risks

Understanding and fully defining the risks of a given technical scenario require both an analysis of the science itself (see intrinsic ethics issues on handling of uncertainty), and a projection as to how the technology could potentially cause harm or otherwise negatively impact human well-being. Risk has two aspects that need to be considered when thinking about a project. Could the research or technology itself present any apparent or immediate risk? Could the technology increase the overall risk profile of a society?

What constitutes a viable risk assessment for energy and environment technologies? Precaution in the face of risk needs to be considered and taken into account in any case, and certain aspects of energy production can present an exceptional risk to human and environmental health. As such, regulation will need to be comprehensive, robust, and conservative with respect to risk projections.

The Precautionary Principle

Precautionary measures mandate that we proceed cautiously (but not necessarily slowly) and deliberatively in the face of high risks coupled with any uncertainties. The precautionary principle in its most simple expression suggests that we plan for worst-case scenarios in the face of high risks coupled with uncertainties. The main idea is that, when faced with taking risks (intended and unintended) that could affect a significant portion of the population or environment, we proceed through the process cautiously and deliberately. The precautionary principle should be invoked when high-risk, irreversible, or catastrophic situations are possible, even at a very low probability.

Figure 1.13: Map of Science that shows how all branches of science are interconnected

Credit: Los Alamos National Laboratory

Embedded Ethical Choices

Values and ethics become embedded within the production of research, oftentimes at the very decision about research topic and question. Such decisions are rarely made within ideal conditions, where resources and time are of no issue. Research is done dependent on deadlines, budgets, peer review feedback, departmental resources, etc. How research is framed, the choice of explanatory frameworks and global assumptions about variables, and the explanations about causal relationships in a given model all present choices that can embed values about representative samples, as is a common question in biomedical or genetic research.

Choice of Research Questions

Research results are inevitably impacted by the scope and range of research questions. Context dependent values can impact problem choice; whether due to individual interests, funding agency interests, or broader societal interests, contextual values become interwoven into research practice. Further, choice of research question can also influence whether or not certain risks are taken into account, or are able to even be considered within the framework of a given nanotechnology research program.

If we knew what it was we were doing, it would not be called research, would it?

Frameworks and Global Assumptions

Interests of the researcher are reflected in accepting certain framework conditions, such as the representational limits of an analysis, or in choosing the values of certain variables, within a model, as being “more” representative of reality than a different variable, model, or limit.

Causal Explanations and Narratives

Causal explanations produce a conception as to what is happening within a given nanotechnology model or analysis. However, many simplifications and reductions are made just to make a model usable, and in doing so, there is no guarantee that a significant causal relationship does not go either unseen or unconsidered.

Figure 1.14: Gray Gold and Red Industrial Machine

Credit: Gray Gold and Red Industrial Machine  by ClickerHappy from Pexels licensed under CC0

How Much Observation, How Simple, and Explanation?

Conducting and publishing research is a process of interpreting observations and describing the results. Questions about research and hypothesis formation point us in a specific direction and guide the interpretation of results. But how do we determine what we are seeing adequately supports our claims? How many observations do we need to make to assume our interpretation is correct? As well, does our research apparatus adequately support our ability to answer our research question in the detail or resolution necessary? How does an observation count if it does not fit our expected results?

Systems are Complex

Complex phenomena require complex models and descriptions. Not adding enough complexity to a research hypothesis could result in oversimplification of a situation, leaving out crucial thresholds or other limits in the system(s) under consideration. Often, in research, a compromise needs to be made, even for reasons of cost, between adequate observations and extremely comprehensive observations (such as sampling across a large site.) All of these choices can potentially lead to a false confidence in projections of model adequacy, which can result in real-world impacts.

The method of science depends on our attempts to describe the world with simple theories: theories that are complex may become untestable, even if they happen to be true. Science may be described as the art of systematic over-simplification—the art of discerning what we may with advantage omit.

Empirical Adequacy and Consistency

Were adequate tests conducted to assure the phenomena observed are consistent, is the study reproducible, or is the instrumentation working within viable parameters and/or limits of observation? As nanotechnology is an emerging field with increasingly finer tolerance, many observations and conceptions of adequacy can change over time.

Simplicity/Scope

What is the scope of the study under consideration? Is the study significantly comprehensive to be relevant to various conditions? Is there detail being lost through the over-simplification of a model or representation?

Proof and Certainty

What constitutes certainty about a given observation? How many times must it be observed to be considered “valid proof” of a particular event? What is considered to be statistically significant for a given event to be occurring? Answering these kinds of questions seems a somewhat arbitrary matter, but consider that what is considered proof in one context is considered a “shadow of doubt” in another context. As well, being wrong in some cases will cost more than being wrong in other cases (as we see in the politics of climate science).

Standards of Proof and Handling of Uncertainties

Standards of proof often incorporate social values. As Anderson writes, “Social scientists reject the null hypothesis (that observed results in a statistical study reflect mere chance variation in the sample) only for P-values\5%, an arbitrary level of statistical significance. Bayesians and others argue that the level of statistical significance should vary, depending on the relative costs of type I error (believing something false) and type II error (failing to believe something true).

Type I and Type II errors:

Type I error: (false positive)

where the test produces a positive result when the negative result is the case, such as in a medical patient testing positive for a disease they do not have. In terms of data analysis, new information falsely changes previous estimates of uncertainty.

Type II error: (false negative)

where the test produces a negative result when the positive result is the case, such as when a medical patient has an ailment that goes undetected by test(s). Regarding data, new information does not correctly change previous estimates of probability of occurrence.

Both types of errors present different costs in different contexts, and result in a choice about values.

In medicine, clinical trials are routinely stopped and results accepted as genuine notwithstanding much higher P-values, if the results are dramatic enough and the estimated costs to patients of not acting on them are considered high enough” (Anderson 2009). Type I and II errors can have significant impacts in energy applications, and will require mindful foresight and consideration both by researcher and peer-reviewers.

Figure 1.14: Chart - Close up Data Desk

Credit: Lukas from Pexels licensed under CC0

Choosing Research

Oftentimes when we travel, we determine where we want to go before we know how we are going to get there. Much the same can be said how we approach research. We know the kind of knowledge we would like to gather, or effect we would like to tease out of a certain set of materials, before we know how we are going to get there. Methods selection itself can shift over the duration of the experimental process (though, hopefully not during an experiment!) of a given investigation. As we travel through the research process, we gather data about observations. This data is shaped by our selection of methods, and also conforms to our classification schemes.

As researchers, how we collect data and how we choose to categorize data are two other processes through which values become embedded in research. This suggests that we should pay close attention to how we justify our methods selection, understand the limitations of what our methods allow us to argue, and are able to justify our categorical and organizational choices.

Rumour has it that the gardens of natural history museums are used for surreptitious burial of those intermediate forms between species which might disturb the orderly classifications of the taxonomist.

Methods Selection

Choice of methods for either data collection and/or analysis reflects the context of the researcher and impact significantly the intellectual merit and framework of the nanotechnology research. “The methods selected for investigating phenomena depend on the questions one asks and the kinds of knowledge one seeks, both of which may reflect the social interests of the investigator” (Anderson 2009). Also, certain methods may not be as applicable in a given situation as others. Comprehensive assessment of methods selection should be clearly stated and justified in the research proposal, included an analysis of possible methods biases.

Classifications and Ontologies

The classification of an observation or phenomena, particularly when the classification strategy is being developed, the adequacy of certain definitions, the granularity of classifications, etc., can have significant impacts in later developments, lead to certain oversights, and even lead to misleading conclusions.