The first post on this blog showed that measuring democracy involves a chain of conceptual and methodological decisions with substantive consequences, namely: how democracy is defined, what data are used, and how those data are aggregated (Freidenberg and Gilas, 2026). In addition, the dimension of institutional resistance to democratic backsliding could also be incorporated when accounting for the state of political regimes. Within this dimension, the judiciary is a fundamental actor when it comes to democratic resistance, although it is often measured less frequently. In response to this gap, this post argues that the available instruments are not designed to answer the most important question about Constitutional Courts in contexts of autocratization: a question that has less to do with how much they are attacked, and much more to do with when and under what conditions these bodies resist.

The Backsliding That Indices Detect Too Late

The literature on democratic backsliding has advanced considerably over the past decade. Bermeo (2016) identified contemporary mechanisms of gradual erosion; Levitsky and Ziblatt (2018) showed how leaders with democratic mandates dismantle institutions from within; and Lührmann and Lindberg (2019) empirically documented that the third wave of autocratization operates under a legal façade, incrementally and in ways that are difficult to detect in its early stages. Ginsburg and Huq (2018), for their part, noted that the two forms democratic decay can take often rely on weakening the checks exercised by courts and parliaments in order to attack the regime, hence the need for institutional designs capable of preventing erosion. In this process, Constitutional Courts are strategic actors, since they can be captured and become instruments of backsliding, or they can resist and contribute to slowing it down.

What this literature has not resolved is how to measure this second possibility. V-Dem offers valuable indicators for capturing attacks on the judiciary, such as high court independence (v2juhcind), judicial purges (v2jupurge), court packing (v2jupack), and government compliance with judicial decisions (v2jucomp) (Coppedge et al., 2023). The problem is that these indicators are designed to measure what executives do against courts, rather than what courts do in response to executives. This asymmetry is not technical but theoretical, since it reproduces, at the level of judicial politics, a bias of unidirectionality by capturing only what rulers do and not the possible responses of judicial bodies. This can be understood as an extension of the broader problem that Freidenberg and Gilas (2026) identify as a linearity bias, referring to the error of treating democratization as an orderly sequence of stages.

A necessary issue when determining what types of responses courts may have involves distinguishing between structural independence and behavioral independence. A court may have formal guarantees of stability and still yield to executive pressure; or it may have a weak institutional design and nonetheless act autonomously at critical moments. This distinction is theoretically important. Ríos-Figueroa and Staton (2014) argued that de facto and de jure judicial independence systematically diverge, and that available measures mix both dimensions without recognizing it. Linzer and Staton (2015) constructed a latent index precisely to try to capture behavioral independence, although its design remains more sensitive to structural conditions than to specific episodes of resistance. Capturing other variables that affect the work of judges and, therefore, the responses courts provide—such as legal socialization or the type of judges—is therefore indispensable.

Four Conditions for a Theoretical-Methodological Framework of Judicial Resilience

An adequate instrument for measuring when courts contribute to halting democratic backsliding should meet at least four conditions.

First condition: capture episodes, not only levels. Current V-Dem indicators produce annual level scores that describe the state of judicial independence, for example, at a given moment. What is needed to analyze resilience are episode indicators; that is, sequences with identifiable beginnings, developments, and outcomes, in which a court faces documented pressure and makes decisions with observable effects on the trajectory of backsliding. The recent work by Nord and Lindberg (2025) on episodes of democratic resistance points in this direction at the systemic level, but it does not move down to the level of specific institutional actors, although it does highlight that a strong judiciary and greater democratic experience reduce the likelihood of democratic breakdown.

Second condition: measure the cost of resisting. A court that issues a ruling against the executive without institutional consequences is not comparable, in terms of resilience, to a court that resists while facing purges, budget cuts, or constitutional reforms directed against it. For this reason, the measurement instrument should incorporate a dimension of assumed institutional risk. Without it, structurally different situations are treated as equivalent, and real resilience is underestimated in contexts where resistance entails high costs—precisely the contexts that are most relevant for the study of backsliding. In the region, for example, it would be misguided to compare the resistance that the Constitutional Court of Colombia may display, protected by a recognized institutional framework, with what the Constitutional Chamber in El Salvador was able to do in 2021 in the face of its purge by a hegemonic executive.

Third condition: distinguish resilience from neutrality. A court that does not systematically rule in favor of the executive, but also does not rule against it, may appear independent, although it is not necessarily so. It may simply be avoiding politically costly cases, or deciding on peripheral areas while yielding on central ones. Behavioral indicators must be directional; that is, they must record whether court decisions, in substantive areas linked to the process of backsliding, go against the interests of the executive in moments of documented pressure.

Fourth condition: integrate the external support environment. Comparative research on judicial resistance in Poland and Hungary (Gyöngyi, 2024) has shown that courts’ ability to resist does not depend only on their own institutional resources, but also on the external coalitions that support them, including judges’ associations, civil society, the legislative opposition, and international actors. A framework of judicial resilience that ignores these coalitions produces incomplete diagnoses. V-Dem includes indicators on civil society mobilization and international support for democracy, but we lack measurements that analytically link them to judicial behavior.

Why Does Measuring Poorly Have Political Consequences?

The asymmetry between the instruments available to measure backsliding and those available to measure resilience is neither a minor nor a neutral problem. Its effect is to produce diagnoses that are systematically biased toward deterioration and to underestimate episodes in which institutions manage to curb erosion. In the case of the judiciary, this bias has direct consequences for how international organizations evaluate the rule of law in a country, for citizens’ assessments of institutions, and for the strategies of actors attempting to defend democracy from within the system.

The 2025 V-Dem report documents that 45 countries are currently experiencing active episodes of autocratization. In most of these processes, judicial deterioration is a central component. The gap that still exists—and on which the discipline must work—is the need for empirical evidence regarding how many courts, among those 45 countries, have resisted at some point, with what resources they have done so, and with what effects on the trajectory of backsliding. That is the question a framework of judicial resilience should be able to answer. Building it requires, first, recognizing that we must move forward in developing instruments for this purpose.

References

Bermeo, Nancy. 2016. “On Democratic Backsliding.” Journal of Democracy, 27(1), 5–19.

Coppedge, Michael, et al. 2026. “V-Dem Codebook v16.” Varieties of Democracy (V-Dem) Project.

Freidenberg, Flavia and Karolina Gilas. 2026. “How Can We Tell Whether a Country Is Democratic?” Changes Within Democracy Blog, June 15. Available at: https://changeswithindemocracy.substack.com/p/how-can-we-tell-whether-a-country.

Ginsburg, Tom, and Aziz Z. Huq. 2020. How to Save a Constitutional Democracy. University of Chicago Press.

Gyöngyi, Petra. 2024. “The Role of Judicial Associations in Resisting Rule of Law Backsliding: Hidden Pathways of Protecting Judicial Independence Amidst Rule of Law Decay.” International Journal of Law in Context, 20(2): 166–183.

Levitsky, Steven and Daniel Ziblatt. 2018. How Democracies Die. New York: Crown Publishers.

Linzer, Drew, and Jeffrey K. Staton. 2015. “A Global Measure of Judicial Independence, 1948–2012.” Journal of Law and Courts, 3(2): 223–256.

Lührmann, Anna, and Staffan I. Lindberg. 2019. “A Third Wave of Autocratization Is Here: What Is New About It?” Democratization, 26(7): 1095–1113.

Nord, Marina, and Staffan I. Lindberg. 2025. “U-turns—The Hope for Democratic Resilience.” Policy Brief, 42: 17–39.

Ríos-Figueroa, Julio, and Jeffrey K. Staton. 2014. “An Evaluation of Cross-National Measures of Judicial Independence.” The Journal of Law, Economics, & Organization, 30(1): 104–137.

About the Author

Patricia Sotomayor Valarezo is a political scientist and lawyer. She holds a PhD in Political Science from the Pontifical Catholic University of Chile. She also holds two postgraduate degrees in Law and she is a member of the Network of Women Political Scientists – #NoSinMujeres. Her research focuses on judicial politics in Latin America, socio-legal studies, and institutional analysis, with an emphasis on high courts and the relationship between law and politics. Her email address is psotomayorv@usfq.edu.ec, and her X account is @PatySValarezo.

This post is part of the project “Changes Within Democracy: Backsliding, Resistance, and Resilience,” developed at the National Autonomous University of Mexico [UNAM/DGAPA PAPIIT IN309526].

The information contained in this post does not necessarily represent the opinion of the National Autonomous University of Mexico, Mexico.

How to cite this post

Sotomayor Valarezo, Patricia. When Do Courts Resist? Institutional Conditions for Measuring Judicial Resilience. Changes Within Democracy Blog, July 1. Available at: https://changeswithindemocracy.substack.com/p/when-do-courts-resist-institutional

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The Challenge of Measuring Democracy[1]

Assessing whether a country is democratic may seem straightforward, especially if one assumes that holding elections, voting, and allowing people to express themselves freely are enough to qualify a regime as democratic. In practice, however, the task is far more complex. Every assessment rests on a chain of conceptual, epistemological, and methodological choices that shape the results in substantive ways. Determining whether a country is democratic—or not—requires paying close attention to how democracy is observed, defined, and measured. This creates tensions between what democracy is, what is actually measured, and what we expect democracy to be. Addressing those tensions involves a series of decisions that condition the outcome and delimit what can be observed.

The main indices and methodological tools developed in comparative politics—such as V-Dem, the Economist Intelligence Unit’s Democracy Index, and Freedom House’s Freedom in the World—share the broad goal of measuring democracy. Yet they begin from different definitions, rely on different methodologies, and sometimes produce markedly different results. Identifying those differences is essential for using these tools appropriately. Clarifying how they differ is the first step toward evaluating political systems more carefully and making sense of the doubts that arise when measurement instruments point in different directions.

This post maps five major methodological decisions that any researcher must make when evaluating democracy: conceptualizing before measuring; identifying biases before collecting data; choosing the type of data to be used; defining and making explicit the aggregation rule; and selecting the measurement instrument. Each of these decisions has practical implications and profound consequences for how democracy is evaluated. Taken together, they also point to a set of unresolved tasks in this research agenda.

First Decision: Conceptualize Before Measuring

Every act of measurement begins with a definition. In the case of democracy, however, that definition is far from settled. The choice among available definitions has direct consequences for what is observed, what is found, and what conclusions can be drawn. The most basic distinction is between minimalist and maximalist conceptions of democracy. Minimalist approaches—whose most influential point of departure is Schumpeter’s definition (1942), later refined in Dahl’s concept of “polyarchy” (1971)—focus primarily on electoral and procedural dimensions: competitive elections, universal suffrage, and the freedom to compete for power. Their main advantage lies in their precision, as they establish relatively clear thresholds for distinguishing democracies from non-democracies. At the same time, however, they tend to downplay dimensions that extend beyond the electoral arena.

Multidimensional definitions seek to capture that broader complexity. The V-Dem project, for example, evaluates democracy through five complementary dimensions: electoral, liberal, participatory, deliberative, and egalitarian democracy (Coppedge et al., 2023; Coppedge et al., 2012). This approach offers a richer picture of democratic performance, but it also introduces new design problems. How should the boundaries between dimensions be drawn? How much weight should each dimension carry in the overall assessment? How can all this information be aggregated into a single number without losing precisely the multidimensional richness that made the approach valuable in the first place? As Munck and Verkuilen (2002) argue, the conceptual decision about how many dimensions to include—and how they relate to one another—cannot be separated from the methodological decision about how to aggregate them. Both are part of the same measurement design problem.

The choice between a minimalist and a maximalist definition should not be driven by data availability, but by the research question. If the question is whether a country is democratic at a given point in time, a procedural concept may be sufficient. If the question is which specific dimension of democracy is deteriorating or recovering, then a multidimensional concept with clearly delimited dimensions is necessary. Using a maximalist definition with tools designed for minimalist concepts—or the other way around—produces invalid results.

Second Decision: Identify Biases Before Collecting Data

Even once conceptual problems have been addressed, the process of evaluating democracy is shaped by several types of bias that should be identified explicitly before data collection begins. Not all of them can be corrected once the research design has already been set.

The first is evaluator bias. When different experts assess the same phenomenon, their judgments may be shaped by the amount and type of information available about different countries, by ideological and cultural biases, and by differences in how they translate qualitative impressions into numerical scores (Bollen and Paxton, 2000). Coder bias may be systematic, may vary over time, and may even create the appearance of democratic deterioration when what has changed is not the regime itself but the standards used to code it (Little and Meng, 2024).

The second is the bias of linearity. Evaluators’ expectations about democratization can also shape their assessments. A fairly common tendency has been to treat democratization—explicitly or implicitly—as a straight, linear process, unfolding through a sequence of stages that gradually culminate in full democracy. Other scholarship, however, has shown that democratization involves advances and reversals (Fukuyama, 1992; Carothers, 2002; Bermeo, 2016), as well as the overlapping of different processes within the political system (Freidenberg and Gilas, 2026). Assuming linearity distorts diagnosis in two ways: it undervalues setbacks by treating them as anomalies, and it overvalues progress by interpreting it as consolidation or even routinization.

The third is regional selection bias. Measurement tools are often designed on the basis of the experience of democracies in the Global North, where democratic erosion tends to unfold in particular ways. This is not necessarily a problem in itself, but it can become a limitation if it is not acknowledged explicitly. In other contexts, democratic setbacks have often operated through more subtle mechanisms that standard indices capture late or poorly: informal politics, the strategic manipulation of electoral rules, the gradual cooptation of oversight bodies without formal constitutional reform, or the progressive weakening of judicial autonomy through successive legal changes, among others. These processes may leave objective indicators apparently stable even as democratic quality erodes in dimensions that indices only detect once deterioration is already well advanced.

Third Decision: Choose the Type of Data

The next decision concerns the type of data to be used. The literature generally distinguishes among three main approaches, each with specific advantages and limitations depending on the research question.

Objective data include verifiable indicators. Their main advantage is replicability (Altman and Pérez-Liñán, 2002): any researcher with access to the same sources should be able to arrive at the same values. This is valuable in itself. Yet objective data also have a clear limitation: not all relevant aspects of democracy can be directly quantified. Informal practices, political culture, and the gradual erosion of unwritten norms often fall outside their reach.

Subjective data are based on expert assessments or citizen perceptions. V-Dem, Freedom House, and the EIU all rely on expert evaluations to capture subtle aspects of democratic performance that objective indicators cannot easily detect. Surveys, in turn, make it possible to approximate how citizens perceive and value democracy. These measures, however, are vulnerable to several forms of bias (Little and Meng, 2024). An evaluator with more information about a given country, or with particular ideological expectations about its trajectory, may produce scores that differ from those of another equally qualified evaluator.

Mixed approaches seek to combine the strengths of both strategies through triangulation. An assessment of press freedom, for example, may combine objective records of imprisoned journalists with expert judgments about self-censorship and survey data on public trust in the media. This combination can yield more robust evaluations, but it also requires explicit criteria for integration and careful reflection on how to handle when different types of data produce contradictory assessments of the same phenomenon.

The choice among objective, subjective, and mixed data is not independent of the research question. If the object of analysis is formal backsliding—legal changes, constitutional amendments, electoral results—objective data are usually more appropriate. If the focus is on the erosion of informal norms, political culture, or the shrinking of civic space, subjective data are indispensable. If the goal is to capture the gap between formal and substantive democratization—one of the most important dimensions in the study of Latin American democracies—then a mixed approach, with explicit triangulation, is the most suitable option.

Fourth Decision: Choose the Aggregation Rules

Reducing multiple dimensions to a single index entails normative choices that are rarely justified by sufficient theoretical grounding, even though these choices have substantive consequences for the results. As Munck and Verkuilen (2002) note, the use of simple averages implicitly assumes that all components are equally important in determining whether a system is democratic. That is a normative decision disguised as a technical one.

The problem is that every aggregation rule assumes a theory about how the components of a concept relate to one another. Are they substitutable? Are they complementary? Are some dimensions essential, such that their absence would negate the democratic character of the system regardless of how well the system performs on other dimensions? That theory is rarely made explicit in the definitions of democracy that provide the conceptual foundation for measurement indices.

Alternatives to the simple average do not solve the problem; they merely relocate it. The geometric mean penalizes low values and reduces compensation across dimensions, but it also assumes that a zero value in any component pulls the aggregate score down to zero. That decision makes sense if one assumes, for example, that the complete absence of free elections invalidates the democratic character of a system, but it does not fit every conception of democracy. Using the minimum value as the aggregation rule maximizes non-compensability, but it discards all the information contained in non-critical dimensions and makes the aggregate score extremely sensitive to measurement error in a single component. Variable-weight formulas shift the problem from implicit equality to the explicit justification of each weight—something that is rarely done with adequate theoretical support.

One consequence of this indeterminacy is that measurement projects drawing on similar sources and related concepts may produce substantially different results for the same cases. Divergences among Freedom House, Polity, V-Dem, and the EIU are not due only to differences in the indicators each project includes. They also stem—and sometimes primarily so—from the aggregation rules they apply. Goertz (2006) put the point clearly: choosing an aggregation rule is as important as choosing the components of the concept, because the aggregation rule defines the logical structure of the concept itself.

A useful recommendation, then, is that before adopting an existing index or constructing a new one, researchers should make explicit how they expect the dimensions to relate to one another. If democracy is understood as a concept whose dimensions are complementary and non-substitutable—if strength in civil liberties cannot compensate for weakness in free elections—then the simple average is theoretically inappropriate. If the dimensions are assumed to be substitutable up to a certain threshold, then the geometric mean or a formula that accounts for interactions may be more suitable. What is not methodologically acceptable is to adopt an aggregation rule without justifying it. This is the most important normative decision in the design, and leaving it implicit does not eliminate it; it merely hides it.

Fifth Decision: Select the Measurement Instrument

The main instruments available for measuring democracy are organized around the definitions of democracy they employ and the kinds of questions they can answer. Freedom House measures procedural aspects of democracy through scores on political rights and civil liberties. It uses an ordinal scale from 1 to 7, expert panel assessments, and fixed weighting. Its temporal and geographic coverage is broad, but its ordinal scale limits its ability to detect gradual changes within each category.

The Economist Intelligence Unit’s Democracy Index classifies political systems using 60 indicators grouped into five categories: electoral process and pluralism, the functioning of government, political participation, political culture, and civil liberties. It has been used both as a primary source and as a tool for cross-validation. The Bertelsmann Transformation Index evaluates progress toward democracy and a market economy through 17 criteria applied by experts across 137 countries. Its emphasis on national context makes it especially useful for capturing region-specific dynamics.

V-Dem places multidimensionality at the center of its approach. It evaluates democracy through five complementary dimensions, more than 350 indicators, expert assessments from approximately 3,000 country experts, and Bayesian statistical models that estimate not only central values but also the uncertainty associated with each estimate and possible coder bias. Its continuous scale from 0 to 1 makes it possible to capture gradual variation that ordinal scales cannot detect. The Democracy Barometer uses standardized scales from 0 to 100 and factor analysis, allowing for more robust comparisons across dimensions with different distributions.

No instrument or methodological tool fully satisfies every requirement. In one way or another, all measurements involve problems, although of different kinds. Some are too highly aggregated; others include conceptually heterogeneous dimensions; still others combine incompatible levels of measurement. The most defensible recommendation—and the one we have adopted in our own research (Freidenberg and Gilas, 2026)—is to use them in a complementary and triangulated way rather than placing full confidence in a single instrument. When different instruments converge in their diagnosis, confidence in the result increases. When they diverge, that divergence is itself a finding that should be explained.

Unresolved Methodological Tasks in the Evaluation of Democracy

One question runs through all these debates and is worth posing directly: should Latin America build its own indices, based on its own definitions of democracy and its own communities of experts? The strongest reason to take methodology seriously is that measuring democracy poorly—or measuring it in biased ways—is not a neutral academic error. It is a methodological trap that can become a political one, with consequences for how citizens evaluate their political systems and the decisions of political actors, as well as for how international organizations assess country risk in a given political regime.

The discussion here points to several tasks for those evaluating democracy, all of which have direct implications for research design. The first is to establish operational thresholds that enable identification of when a change in democracy begins and when it ends. The second is to integrate formal and informal indicators. Closing this gap is necessary for producing more accurate diagnoses of the state of democracy, especially in regions where democratic setbacks operate primarily through gradual and informal mechanisms. The third is to build instruments that can distinguish more precisely among different kinds of change within democracy, whether in the form of backsliding, resistance, or resilience.

The field of democratic backsliding already has relatively consolidated definitions and reference instruments, especially those centered on V-Dem (Knutsen et al., 2024). By contrast, the field of democratic resilience remains unsettled: it operates with competing definitions and lacks quantitative instruments of its own quantitative instruments. This asymmetry produces diagnoses that are systematically tilted toward deterioration (Freidenberg and Gilas, under review). Addressing it requires more than building comparable instruments for all three processes. It also requires an integrated theory of how they relate to one another: whether resistance is a component of resilience, whether resilience is the accumulated result of successful episodes of resistance, or whether backsliding, resistance, and resilience are analytically independent dimensions that coexist in different combinations. That integrative theory remains pending—and building it is the task we are currently working on.

[1] This text is based on the paper “How Should We Evaluate Changes Within Democracy? A Critical Assessment of Different Tools for Measuring Political Systems,” prepared for the XIII Congress of the Latin American Political Science Association (Buenos Aires, July 2026), as part of the project “Changes Within Democracy: Backsliding, Resistance, and Resilience,” developed at the National Autonomous University of Mexico [UNAM/DGAPA PAPIIT IN309526].

References

Altman, David and Pérez-Liñán, Aníbal (2002). Assessing the quality of democracy: Freedom, competitiveness, and participation in eighteen Latin American countries. Democratization, 9(2), 85–100. https://doi.org/10.1080/714000255

Bermeo, Nancy (2016). On democratic backsliding. Journal of Democracy, 27(1). https://doi.org/10.1353/jod.2016.0012

Bollen, Kenneth A. and Paxton, Pamela (2000). Subjective measures of liberal democracy. Comparative Political Studies, 33(1). https://doi.org/10.1177/0010414000033001001

Carothers, Thomas (2002). The end of the transition paradigm. Journal of Democracy 13.

Coppedge, Michael, Gerring, Jon, Knutsen, Carl Henrik, Lindberg, Staffan I., Teorell, Jan, Altman, David, Bernhard, Michael, Cornell, Agnes, Fish, M. Steven, Fox, Aaron, Gjerlow, Haakon, Glynn, Adam, Grahn, Sandra, Hicken, Allen, Kinzelbach, Katarina, Krusell, Joshua, Marquardt, Kyle L., McMann, Kelly, Mechkova, Valerya and Wilson, Steven L. (2023). V-Dem [Country-Year/Country-Date] Dataset v13. Varieties of Democracy Project. https://doi.org/10.23696/vdemds23

Coppedge, Michael, Gerring, John and Lindberg, Staffan I. (2012). Variedades de democracia (V-Dem): un enfoque histórico, multidimensional y desagregado”. Revista Española de Ciencia Política, vol. 30.

Freidenberg, Flavia and Gilas, Karolina. ¿Cómo evaluar los cambios dentro de la democracia? una evaluación crítica de diversas herramientas de medición de los sistemas políticos. In Freidenberg, Flavia (Eds.). Las democracias en América Latina: retrocesos, resistencias y resiliencias. Ciudad de México. Manuscrito en dictaminación.

Freidenberg, Flavia and Gilas, Karolina (2026). Midiendo la democracia: una propuesta de índice multidimensional de cambio democrático. Revista Latinoamericana sobre Democracia, 2(2). https://doi.org/10.22201/iis.rld.2026.2.16

Goertz, Gary (2006). Social Science Concepts: A User’s Guide. Princeton University Press.

Huntington, Samuel P. (1991). The Third Wave: Democratization in the Late Twentieth Century. University of Oklahoma Press.

Knutsen, Carl Henrik, Marquardt, Kyle L., Seim, Brigitte, Coppedge, Michael, Edgell, Amanda B., Medzihorsky, Juraj, Pemstein, Daniel, Teorell, Jan, Gerring, John and Lindberg, Staffan I. (2024). Conceptual and measurement issues in assessing democratic backsliding. PS: Political Science & Politics, 57(2). https://doi.org/10.1017/S104909652300077X

Little, Andrew T. and Meng, Anne (2024). Measuring democratic backsliding. PS: Political Science & Politics, 57(2). https://doi.org/10.1017/S104909652300063X

Munck, Gerardo L. and Verkuilen, Jay (2002). Conceptualizing and measuring democracy: Evaluating alternative indices. Comparative Political Studies, 35(1). https://doi.org/10.1177/001041400203500101

Schumpeter, Joseph A. (1942). Capitalism, Socialism and Democracy. Harper & Brothers.

Flavia Freidenberg is a researcher at the Institute for Legal Research of the National Autonomous University of Mexico and a member of the Network of Women Political Scientists – #NoSinMujeres.

Karolina Gilas is a professor at the Faculty of Political and Social Sciences of the National Autonomous University of Mexico and a member of the Network of Women Political Scientists – #NoSinMujeres.

How to cite this post

Freidenberg, Flavia y Karolina Gilas. 2026. How Can We Tell Whether a Country Is Democratic? Changes Within Democracy Blog, 15 de junio. Disponible en: https://changeswithindemocracy.substack.com/p/how-can-we-tell-whether-a-country

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