1. Introduction

Deliberative minipublics assemble a group of ordinary citizens, expose them to relevant information and positions on an issue and facilitate deliberation, which then yields results that are used as input in decision-making (Fung 2003). Representativeness and diversity are frequently cited as desirable characteristics of participant groups in deliberative minipublics (Brown 2006; Caluwaerts & Ugarriza 2012; O’Doherty & Burgess 2013; Rowe & Frewer 2000). However, representativeness and diversity can be interpreted in multiple ways. Statistical representativeness — wherein the distribution of relevant characteristics in the sample resembles that in the general population — is often distinguished from cross-sectional representativeness, wherein all relevant characteristics present in the population are also found in the sample but not necessarily in the same proportions (Brown 2006; Goodin & Dryzek 2006). Moreover, diversity may refer to a wide variety of perspectives or demographic categories, or to the inclusion of members of socially marginalised or discriminated-against groups (Steel et al. 2018). Representativeness and diversity may also come apart. For instance, if one viewpoint is statistically predominant in the population, then maintaining cognitive diversity would seem to require a nonrepresentative sample. Consequently, a better understanding of the complex interactions between diversity and representativeness is crucial for the future design of deliberative minipublics.

In this article, we suggest a purposive design approach that emphasises articulating the aims of deliberative minipublics and linking them to concepts of representativeness and diversity, which can then guide recruitment strategies. According to this approach, deliberative minipublics may differ in their aims, and a single minipublic may have mixed or hybrid aims. The appropriate concepts of representation and diversity, therefore, should be chosen in light of this heterogeneity. Our approach contrasts with an influential position on representation in deliberative minipublics according to which political equality (understood as the equal chance of influencing political outcomes) justifies interpreting representation in a statistical sense and recruiting participants via random sampling (Fishkin 2018; Smith 2009). Whereas this approach attempts to proceed deductively from universal principles of democracy, we take the aims of deliberative minipublics to be variable and the relevant concepts of representativeness and diversity to vary accordingly.

We distinguish three goals of deliberative minipublics. First, some seek to approximate the counterfactual public will, that is, to estimate the distribution of preferences the public would have regarding a menu of policy options if the public were to be informed and were to deliberate on the subject. This is frequently the aim of deliberative polling, especially as advocated by James Fishkin (2018). Second, deliberative minipublics sometimes aim to elicit a range of perspectives, that is, to explore the views or concerns that emerge from informed deliberation among some suitably diverse sample of the population. This aim is briefly suggested by Mark Brown (2006), and is illustrated by an actual deliberative minipublic that we describe below. The third and final aim of deliberative minipublics we consider is what we call co-creating recommendations. This aim is exploratory and decision oriented: it encourages the exploration and development of novel proposals, and prompts participants to develop collective recommendations and reasons for them. It differs from the first aim in not presuming a preset menu of options, and from the second in not being satisfied to merely record a range of disparate perspectives. While these three aims are inspired by actual deliberative minipublic formats, to our knowledge they have not been explicitly formulated in previous works on public deliberation.

We advance the following claims in connection with these three aims. First, recruiting a sample of participants that is statistically representative of the relevant population should be a priority given the first aim (approximation of the counterfactual public will) but not necessarily so for the other two. This claim is consistent with the observation that, among deliberative minipublic formats, only deliberative polling strives for statistical representativeness (see Goodin & Dryzek 2006). But our proposal furthers the discussion by providing a principled explanation of why these differences should not merely be seen as defects of deliberative minipublics that do not aim to recruit statistically representative samples. Second, for all three aims, there can be good reasons to include certain groups in proportions greater than would be expected from a random sample. We argue that political equality, while sometimes cited as an argument for random sampling (Fishkin 2018), in fact provides a powerful reason to oversample minority groups when populations have been constituted in starkly inequitable ways. Our third claim is that mixed aims of deliberative minipublics can justify hybrid recruitment strategies. Our approach entails that random sampling, while having its place, should not be treated as a definitional criterion (pace Smith 2009) nor as an indicator of quality (pace Fishkin 2018) of deliberative minipublics. It also suggests that organisers of deliberative minipublics should be explicit about their aims and should recognise that these aims are often mixed, in which case, attaining them may require hybrid recruitment strategies.

The article is organised as follows. In Section 2, we review the existing literature on representation and diversity in deliberative minipublics and identify what we consider as shortcomings of existing arguments. In Section 3, we propose and defend our purposive design approach. First, Section 3.1 elaborates the three aims already alluded to earlier, and illustrates each with an example of an actual deliberative mini-public. Next, Section 3.2 develops our three claims and the consequences they entail. Finally, Section 4 concludes by reemphasising the central themes of our purposive design approach and contrasting it with alternatives.

2. Statistical Representativeness Versus Cross Sections

While a substantial literature exists on the concept of democratic representation, much of it focuses on electoral representation, for instance, as found in legislative assemblies (Brown 2006; Urbinati & Warren 2008). In contrast, participants in deliberative minipublics are not usually elected, and hence typically lack the legal authorisation to make decisions on behalf of broader publics¹ and usually cannot be held accountable by them. We focus, then, on nonelectoral concepts of representativeness. Within the nonelectoral category, Nadia Urbinati and Mark Warren (2008) distinguish between self-authorised and citizen representatives. In the former case, an individual or organisation takes it upon itself to publicly represent the interests of a particular constituency defined, for instance, by ethnicity, religion or profession. Citizen representatives, in contrast, do not present themselves as representatives of others and only act in this capacity within the confines of the deliberative mini-public. Citizen representatives may self-select to participate or be recruited by deliberation organisers.² Although self-authorised representatives are included among the participants in some types of deliberative minipublics, citizen representatives are usually the majority. And in some minipublic designs, self-authorised representatives are excluded from participating as deliberators, although they may be invited to express their positions and arguments (Kaldec & Friedman 2007). Our focus here, then, is on the possible senses in which a group of citizen representatives can be said to represent a larger populace. For convenience, in what follows we use ‘representativeness’ specifically in relation to nonelectoral citizen representatives.

More than one notion of representativeness can be found within the literature on deliberative minipublics. According to an influential proposal, representativeness requires that the statistical distribution of demographic or other relevant characteristics in the minipublic matches that of the population. This is typically taken to motivate random selection of participants on the principle that a large random sample will approximate the population distribution, and that in an ideal democracy all citizens should have an equal chance of influencing the political process (Brown 2006; Dryzek & Niemeyer 2008; Caluwaerts & Ugarriza 2012; Fishkin 1995, 2018; Fung 2003; Hainz, Bossert, & Strech 2016; Landemore 2013). We call this statistical representativeness. An alternative is that representativeness only requires a ‘cross-section,’ wherein relevant demographic categories and perspectives are represented in the minipublic but not necessarily in the same proportions as in the population at large (Brown 2006; Goodin & Dryzek 2006; Hainz et al. 2016). We label this position cross-sectional representativeness. Cross-sectional representativeness is sometimes motivated by the aim of ensuring a diversity of perspectives in deliberation, which may be swamped in a statistically representative group if one perspective tends to dominate in the population (Brown 2006).

We begin by examining arguments given in favour of statistical representativeness and random sampling. Fishkin (2018) argues that statistical representativeness achieved via random sampling gives each citizen an equal chance of participation in the deliberative mini-public, and therefore embodies the democratic ideal of political equality:³ ‘The idea is to equally count everyone’s views under conditions where they can really think in order to give expression to a meaningful public will. Hence, we will draw on work that combines random sampling to help achieve political equality with conditions facilitating in-depth, thoughtful discussion’ (Fishkin 2018: 7). Fishkin cites the ancient Athenian nomothetai, in which citizens were selected by lot to consider proposed changes to laws, as a precursor of his approach (Fishkin 2018: 22). However, we argue that political equality does not justify random sampling for two reasons.⁴ The first is that random sampling does not correct for social inequities that may skew sampling frames and underlying populations. The second is that ‘random sampling’ is a somewhat misleading term to describe the process: individuals are randomly invited to participate in the deliberation, but those who accept need not be a random or representative sample.⁵

Random sampling may fail to give everyone an equal chance of participating because social inequities can skew sampling frames and underlying populations from which individuals are selected. A sampling frame is a list of individuals that is taken to correspond to an underlying population. For example, suppose the aim is to recruit a sample of registered voters in the state of California. The population then consists of registered voters in California, and the sampling frame is a list of registered voters maintained by the state elections board. In this case, social inequities (i.e., voter suppression) can skew both the population and the sampling frame. Voter suppression can skew the underlying population by creating unfair obstacles to voter registration for certain groups of people (e.g., by closing government offices where people can register to vote in predominantly African American counties). In this way, social inequities may affect who is and who is not a registered voter. Moreover, voter suppression can directly distort the sampling frame, for instance, if inaccuracies in the voter registration list (e.g., a registered voter being removed from the list in error) do not occur at random but are more likely to disenfranchise minority voters. As Carol Anderson (2018) documents, suppression of minority voter registration, especially African Americans, has been a persistent phenomenon in the United States since the reconstruction era, and has experienced a revival following the 2013 Supreme Court decision in Shelby County v. Holder that weakened the Voting Rights Act of 1965. In such circumstances, a random sample drawn from a list of registered voters will not give everyone an equal chance of being invited because not everyone had an equal chance of being on the list.

Voter suppression is noted by Fishkin as one of the ills of contemporary democracy (Fishkin 2018: 4). Yet some deliberative polls discussed by Fishkin are based on samples of registered voters (Fishkin 2018: Chapter 3). So, when voter suppression is a problem, voter registration lists and random samples drawn from these lists will be skewed by that inequity. In general, social inequities are likely to distort sampling frames, such as lists of registered voters, people with stable addresses, people who respond to the census and so on (see Wojciechowska 2019). To our knowledge, Fishkin does not address the problem that sampling frames and underlying populations might be skewed by social injustices. However, Fishkin (2018) argues that in actual deliberative polls, there is no evidence of distortions resulting from more advantaged people dominating the process, or from polarisation in which deliberation drives group opinion to a more extreme version of the view the majority initially preferred. Not all research on discursive equity in deliberative polls agrees that their internal processes are as free of distortions as Fishkin suggests: participation inequality has been measured (Gerber 2015), and some strategies have been used to counteract it, such as enclave deliberations for marginalised groups (Abdullah, Karpowitz, & Raphael 2016). However, we do not pursue this issue here. Instead, we note that distortions arising in the process of deliberation, while an important concern, are separate from the impact of societal inequities on those who are deliberating in the first place. Similarly, Athenian nomothetai, due to their exclusion of women and slaves, would not have satisfied the principle of political equality, even if their members had been randomly selected from adult male citizens and their internal processes had been free of distortions.

Second, even if everyone has an equal chance of being invited, it does not thereby follow that all have an equal chance of participating (Smith 2009; Wojciechowska 2019). Such considerations can be used to motivate stratified random sampling, which several scholars have advocated (Rowe & Frewer 2000; Longstaff & Burgess 2010; O’Doherty & Burgess 2013). In this approach, proportions of participant categories (e.g., along demographic lines) might be set by a sampling frame (e.g., census data), and then populated by individuals who have accepted randomly issued invitations to participate in the deliberation. For instance, suppose that 1,000 people are randomly issued invitations, and among those, 100 accept, with a gender breakdown of 60 women and 40 men. If the desired number of participants is 50, then 25 of the men and 25 of the women among those who have accepted the invitation might be selected to participate. Thus, stratified sampling might correct for statistical nonrepresentativeness arising from bias regarding who accepts invitations, or perhaps from random variation in small samples (Smith 2009: 81). Note that as described, stratified sampling does not address the concern that social inequities may skew the sampling frame. However, it could do so, for example, by increasing the proportions of groups thought to be underrepresented due to inequities that distort the sampling frame.

The considerations in the previous paragraph further weaken the link between political equality and recruitment to deliberative minipublics by randomly issued invitations. Random sampling does not counteract inequities that may skew the sampling frame or bias who accepts invitations to participate. Thus, when there are good reasons to think that such effects are present, random sampling is not merely insufficient to achieve political equality, it is also not necessarily the best way to approximate it. If inequities and ensuing biases are known, then stratified sampling or other approaches that correct for them may be more likely to result in everyone having an equal chance to participate. For instance, consider what Fung terms an ‘affirmative action’ approach to recruitment, wherein organisers attempt to achieve ‘demographic representativeness by publicizing the event in communities that would otherwise be underrepresented’ (Fung 2003: 342). Approaches that target relevant but likely to be underrepresented groups could be combined with random sampling. For example, a group that consists of five percent of the population might receive 10 percent of the invitations if they are half as likely to accept. Difficulties can certainly arise for methods that aim to counteract biases, for instance, if efforts to increase the numbers of one underrepresented group inadvertently exclude others (Smith 2009: 81). However, such challenges fail to rescue political equality as an argument for random sampling. Even if methods to counteract inequities that bias participation in deliberative minipublics are imperfect, the may be more likely to equalise the chance of participation than random invitations.

In addition, some scholars express ambivalence about statistical representativeness, especially due to its potential effects on minority perspectives and demographic groups. For example, Gene Rowe and Lynn Frewer (2000) state that participants should be representative of the broader public, but urge that caution should be exercised about disenfranchising marginalised groups of society, and that members of all affected communities should be present. Despite these qualifications, they conclude by emphasising that representation should consider the distribution of views, and caution that in a small sample there is a risk that representing every viewpoint might diminish the influence of the majority (Rowe & Frewer 2000: 12). Similarly, some have suggested that statistical representativeness should be balanced against diversity. For example, Kieran O’Doherty and Michael Burgess write that ‘random sampling with attention to maximizing the diversity of participants seems to offer the best option for constituting a deliberating minipublic’ (O’Doherty & Burgess 2013: 60). These discussions suggest that diversity might be a reason to depart from statistical representativeness to some extent. However, O’Doherty and Burgess leave unclear when statistical representativeness should be sacrificed in the name of diversity, to what extent, and why.

Let us turn, then, to cross-sectional representativeness. The primary motivation for this conception of representativeness is that it may result in greater diversity, in both demographics and perspectives, than statistical representativeness would. Suppose that there are 15 relevant perspectives to represent in a deliberative minipublic that will convene 50 participants, but that two perspectives account for 80% of the general population. In this case, a sample that is statistically representative with regard to perspectives cannot include all 15 of them, assuming that each person has no more than one perspective. The two most popular perspectives take up 40 of the 50 seats, leaving only 10 seats for the remaining 13. Cross-sectional representativeness, then, is naturally associated with a concept of diversity according to which greater diversity entails a larger number of relevant characteristics among the deliberators. Thus, in the example, deliberators are more diverse if all 15 perspectives are present rather than only 12 (i.e., the 2 most popular plus 10 others). Consequently, achieving cross-sectional representativeness can require reducing the proportions of majority groups, as defined by perspectives, demographics or other characteristics judged relevant to deliberation. Arguments for cross-sectional representativeness in deliberative minipublics often suggest that greater diversity is likely to improve epistemic quality or validity of deliberation (Brown 2006; Bohman 2006; Caluwaerts & Ugarriza 2012).

However, the argument for cross-sectional diversity encounters some difficulties. One is that the number of relevant perspectives may be too large for it to be practical to include all of them. Questions about which perspectives are most important to include and why they should be included seem unavoidable, yet cross-sectional representativeness provides no guidance on how such judgments should be made. The second difficulty concerns the implicit conception of diversity as simply an increasing function of the number of relevant characteristics present. Diversity might instead be thought to depend on the presence of specific characteristics, such as perspectives associated with socially marginalised groups (Steel et al. 2018). Moreover, in some cases, the epistemic advantages of diversity might be more closely linked to the presence of certain perspectives that are less commonly heard from — for instance, of those with relevant lived experiences — than with the number of different perspectives (Harding 2015). Both of these difficulties can be elaborated in connection with intersectionality.

While its interpretation is much debated (Carastathis 2014; Crenshaw 1989, 1991; Hancock 2016; Hill Collins & Bilge 2016), Marta Wojciechowska (2019) characterises intersectionality, in the context of deliberative minipublics, as emphasising that social categories are multiple and dynamic. They are multiple in the sense that people fall under several overlapping social categories (e.g., gender, ethnicity and sexual orientation), and dynamic in that categories need not be viewed as discrete (e.g., gender may be viewed as a continuum along which a person may move rather than as a fixed dichotomy between male and female). Intersectionality is also specifically focused on cases in which multiple socially disadvantaged attributes apply to a person (e.g., black female homosexual), and suggests that the total impact of overlapping disadvantages tends to be greater than the sum of the effects considered separately (Wojciechowska 2019). In Kimberlé Crenshaw’s (1989) classic formulation, failure to appreciate intersectionality results in implicitly understanding sexism as it is experienced by white women and racism as it confronts black men, making discrimination faced by black women difficult to communicate and remedy.

A multiple and dynamic approach can be expected to increase the number of categories to be represented. For example, only three binary attributes (e.g., male versus female, black versus white, heterosexual versus nonheterosexual) produce eight intersections, each of which might be represented, while four binary attributes result in 16 intersectional categories and so on. The possibility that attribute categories might be represented as dynamic rather than discrete accentuates this situation. At a minimum, accommodating the potentially dynamic nature of categories involves adding an additional category for those who prefer not to identify according to a commonly presumed set of discrete options. Thus, in the case of gender, a ‘none of the above,’ ‘trans,’ or ‘X’ category might be listed along with male and female. Such an approach would significantly increase the number of possible intersections,⁶ despite being rather inadequate from the perspective of intersectionality. For instance, simply adding a catchall category for nonbinary gender foregrounds the binary categorisation as the default and does not specify an alternative dynamic mode of identification. A more thorough going intersectional approach might permit respondents to choose between discrete and dynamic modes of gender identification and provide a format for each type of response. A more adequate inclusion of dynamic modes of representation, then, would increase the number of possible intersectional categories even further.

Intersectionality, then, draws attention to the first difficulty for cross-sectional representativeness noted above: the number of categories may be too large, making it impractical to represent all of them. Intersectionality’s focus on overlapping disadvantaged categories could limit the number of intersections that it is important to represent. However, such an approach appears to be at odds with the underlying rationale for cross-sectional representativeness, which does not privilege representing some categories over others, and conceives of diversity as a function of the number of categories present. A focus on groups subject to intersecting social disadvantages, then, highlights the second difficulty for cross-sectional representativeness, namely, that some categories or intersections may be more important than others as far as diversity is concerned.

In sum, at least two concepts of representativeness — statistical and cross-sectional — are discussed in the literature on deliberative minipublics, and the rationale for each leaves something to be desired. Statistical representativeness is typically linked to random sampling, which in turn is justified by its appeal to political equality as a fundamental democratic principle. Yet political equality does not justify random sampling if the relevant population is skewed by social inequities. In such cases, achieving political equality may require compensating for known inequities that are likely to be reproduced in random samples. Cross-sectional representativeness, on the other hand, faces the problem that the number of relevant categories can be expanded to such an extent that it is unfeasible to include all of them. In such circumstances, the question of which categories are most important to represent is unavoidable. In the next section, then, we develop an alternative approach to representation in deliberative minipublics.

3. Purposive Design Approach

Our proposal suggests a flow from the aims of a deliberative mini-public to relevant conceptions of representativeness and diversity, and from there to appropriate recruitment strategies. The links between each of these steps should be thought of as guidance rather than as a deterministic process, since factors besides those we highlight may also be relevant. Nevertheless, we claim that the guidance provided by our purposive design approach is informative from both a theoretical and practical point of view. We also claim that our proposal is a theoretical improvement over the approaches discussed above, and that it can assist practice by encouraging more justified and transparent rationales for recruitment strategies in the design of deliberative minipublics. Developing our approach requires distinguishing among several possible aims of deliberative minipublics (Section 3.1), and explaining how those aims connect to different concepts of representation and recruitment strategies (Section 3.2).

4. Conclusions

Representativeness and diversity are desirable features of participant groups in deliberative minipublics. However, both of these concepts can be interpreted in more than one way, and furthermore the two can lead in different directions. That in turn creates theoretical and practical conundrums about how participants in deliberative minipublics should be recruited. A better understanding of representativeness and diversity, and how to balance them in specific contexts, is therefore central to the future design of deliberative minipublics.

In this article, we have addressed this issue by developing a purposive design approach to representation, according to which the concept of representation appropriate for a deliberative minipublic depends on its aims. This approach proceeds pragmatically from the consideration of possible goals of deliberative minipublics rather than attempting to deduce a conception of representation and a recruitment strategy from a basic principle of democracy, such as political equality. While we do not doubt the importance of political equality, we think the distance between it and the variable circumstances of deliberative minipublics is too great for a deductive approach of this kind to succeed. For example, the link between random sampling and political equality in a deliberative minipublic depends on whether the aim is to approximate the counterfactual public will and on whether the population and sampling frame are skewed by inequities. Our approach also contrasts with arguments for cross-sectional representation in that it can naturally accommodate distinct notions of diversity. Diversity might refer to a larger number of perspectives or to perspectives of socially marginalised people whose experiences are particularly relevant to the topic of deliberation, and which way of thinking about diversity is most relevant can depend on the aims of the deliberative minipublic and details of the context. Finally, while some authors have noted trade-offs among the aims of deliberative minipublics (Beauvais & Bächtiger 2016; Caluwaerts & Ugarriza 2012), our proposal advances the discussion by exploring more systematically the possible aims of deliberative minipublics and linking these aims to concepts of representation and thereby to recruitment strategies.

Competing Interests

The authors have no competing interests to declare.

Author Information

Daniel Steel is an associate professor at the University of British Columbia in the Centre for Applied Ethics in the School of Population and Public Health. His research interests lie in ethical and epistemic issues that arise at the crossroads of science and public policy.

Naseeb Bolduc is a graduate student at the University of British Columbia in the School of Population and Public Health. Her research interests are primarily centred around justice and values in public health.

Kristina Jenei is a graduate student at the University of British Columbia in the School of Population and Public Health. Her research interests are centred around uncertainty in priority-setting (mainly in oncology) and ways to meaningfully involve the public in the world of decision-making.

Michael Burgess is a professor at the University of British Columbia in the Centre for Applied Ethics in the School of Population and Public Health, and in the Department of Medical Genetics. He is also Associate Provost, Strategy, on the UBC Okanagan Campus. His research focuses on the design and implementation of deliberative public engagement for ethical and policy advice.