dataesr/scientific-paragraphs-categorization huggingface dataset:
With a proliferation of scholarly work focusing on populist, far-left, and far-right parties, questions have arisen about the correct ways to ideologically classify such parties. To ensure transparency and uniformity in research, the discipline could benefit from a systematic procedure. In this letter, we discuss how we have employed the method of 'Expert-informed Qualitative Comparative Classification' (EiQCC) to construct the newest version of The PopuList (3. 0)a database of populist, far-left, and far-right parties in Europe since 1989. This method takes into account the in-depth knowledge of national party experts while allowing for systematic comparative analysis across cases and over time. We also examine how scholars have made use of the previous versions of the dataset, explain how the new version of The PopuList differs from previous ones, and compare it to other data.
Seeking to address this problem, The PopuList database has become a resource for researchers in academia and beyond. The PopuList includes all European parties from thirty-one countries that can be classified as either populist, far left, or far right and have won at least one seat or at least 2 per cent of the vote in national parliamentary elections since 1989. It classifies these parties on the basis of their core ideological attributes in line with the so-called ideational approach  (Mudde 2017). Euroscepticism has been added as a secondary coding category in The PopuList 3. 0 (that is, we have only examined Euroscepticism among parties that are either populist and/or far left/right). 1 The first two versions of The PopuList were launched in 2019 and 2020  (Rooduijn et al. 2019). The most recent iteration of the database was launched in 2023 and provides not only a full update on recent elections but also a series of detailed country reports that discuss the rationale for including parties in the list and justify decisions made about borderline cases.
In this contribution, we first explain EiQCC in more detail and provide a brief overview of the history of The PopuList and the ways in which it brings the principles of EiQCC into practice. Next, we analyse how existing studies have used The PopuList. We then present an overview of The PopuList 3. 0 dataset and offer insights from a comparison of populism with Political Parties Expert Survey (POPPA) data  (Meijers and Zaslove 2021). We conclude with a brief discussion of the merits and limitations of The PopuList.
Expert-informed Qualitative Comparative Classification (EiQCC) Several studies offer useful recommendations for party classification decisions  (Mair and Mudde 1998). Strikingly, however, systematic approaches to comparative party classification that both consider in-depth information about specific cases and provide a general overview across cases remain rare. Large-scale comparative research faces a trade-off between rigorous classification on the one hand and detailed case knowledge on the other. It is challenging to organize a classification procedure that takes into account country and party particularities that is, at the same time, also comparative across cases and over time  (Mair and Mudde 1998, 225). The PopuList uses a method that relies on both comparativists and country experts to generate data on particular cases.
As stated, we term this method EiQCC. This method builds on and formalizes previous approaches that have classified Eurosceptic parties (Taggart and Szczerbiak 1 Euroscepticism is not only limited to our main parties of interest, which are populist/far-left/far-right parties (for example, Taggart and Szczerbiak 2013). However, identifying all Eurosceptic parties would significantly increase the size of our database and distract from the main focus of our project. 2018) and populist parties (  Van Kessel 2015). What makes this approach particularly suitable for party classification is the reliance on in-depth knowledge of scholars with specialized expertise, both in terms of the cases at hand (the countries and parties) and the theoretical and conceptual knowledge underlying classification.
The PopuList reveals how the various ideational attributes are, in practice, combined in individual parties and thus form key ideological components of specific sets of parties. EiQCC is thus an appropriate method for using either/or classifications  (Sartori 1970)  as it attributes membership of political phenomena to definite sets. Although the EiQCC method relies on country experts, it is not an expert survey of parties (see  Steenbergen and Marks 2007). The goal of these expert surveys is to provide a measure of parties' policy positions. There are several recent examples of datasets based on this latter method, including measurements of populism in political parties  (Meijers and Zaslove 2021; Norris 2020; Polk et al. 2017). EiQCC differs from expert surveys in at least two fundamental ways.
Key Characteristics of EiQCC As can be deduced from the above discussion, the method consists of five main components. EiQCC is (1) expert-driven, (2) comparative, (3) qualitative and holistic, (4) dynamic, and (5) iterative and collaborative. First, while we acknowledge that the term 'expert' is loaded, we use it to refer to scholars who have published academic work on the categories of interest. This applies to the team of comparativists in charge of EiQCC and also to consulted colleagues, whom we call 'country experts'. In the case of The PopuList, the latter are not just experts on the politics of specific countries but experts on populism and the far left/far right in a given country. This ensures a sufficient degree of theoretical depth and data reliability that offers confidence and authority in the classification.
Third, the experts classify rather than quantify the data. Our aim when highlighting the qualitative dimension of our method is to emphasize that our measure is dichotomous, and we classify parties holistically on the basis of their ideology or set of ideological attributes. This differentiates us from expert surveys that average expert scores. The objective here is to give each party an overall label and offer a classification rationale that can be used by other researchers when establishing their own datasets. Our qualitative classification allows us to both identify and engage with borderline cases, which we discuss in the country reports included in version 3. 0. Fourth, the classification of individual parties is done dynamically; that is, taking into account what the party as a whole says and does over time.
This is in contrast to datasets that use one prominent party source (for example, election manifestos) or focus on one specific individual (for example, the party leader) to classify parties at a given point in time. While we assume that political parties are unitary collective actors, we allow for the possibility of different ideological factions within the party and for over-time change, in which case we classify on the basis of the dominant faction and trend. Fifth, EiQCC is fundamentally an iterative and, thereby, a collaborative process. This allows us to validate our classification in multiple steps: initially through review by (at least two) individual country experts and then through deliberation among the entire PopuList team. Each case goes through at least four stages of classification and scrutiny.
The PopuList The PopuList 1. 0 and 2. 0 The PopuList is the offspring of a collaborative project between one of this letter's authors and the British newspaper The Guardian in 2018. The result of this project was a unique database of populist, far-left, and far-right parties, some of which were also Eurosceptic, which was peerreviewed by thirty-five leading scholars in the field. The PopuList 1. 0 database categorized all parties that obtained at least 2 per cent of the vote in at least one national legislative election across thirty-one European countries between 1998 and 2018. It employed the following definitions: Populist parties: parties that endorse the set of ideas that society is ultimately separated into two homogeneous and antagonistic groups, 'the pure people' versus 'the corrupt elite', which argues that politics should be an expression of the volonté générale (general will) of the people  (Mudde 2004 ).
This includes both 'hard Euroscepticism' (that is, an outright rejection of the entire project of European political and economic integration and opposition to one's country joining or remaining a member of the EU) and 'soft Euroscepticism' (that is, contingent or qualified opposition to European integration)  (Taggart and Szczerbiak 2013). Bearing in mind the grey area between 'radical' and 'extreme' left/right (March 2011; Mudde 2007), we employ the umbrella concepts 'far left' and 'far right'  (Pirro 2023). These categories include both 'radical' and 'extreme' parties. The database was used for several articles in The Guardian in 2018. 3  After the collaboration came to an end, providing a comprehensive and updated database on these parties seemed a worthwhile service to both journalists and academics.
In 2019, a few steps were made to systematize these efforts: The PopuList team (which consists of eight comparativists from across Europe) was formed, the database was updated, and the project website was launched. One year later, in 2020, we launched version 2. 0 of the database. This new version extended the previous list in several ways: (1) it went further back in time (1989 instead of 1998); (2) it broadened the criteria of inclusion (it now also included parties that never obtained 2 per cent of the vote but have, nonetheless, been represented in their country's national parliament at least once); (3) it was updated to include all relevant parties until 1 January 2020; (4) it added a borderline dummy to all classifications, indicating uncertainty among experts; and (5) the data had been linked to other databases (enabling linkage to ParlGov, the Comparative Manifesto Project, and Party Facts).
Use of The PopuList to date According to Google Scholar, The PopuList was cited approximately 350 times between 2019-22, suggesting it has become a valuable classification tool for researchers working on European political parties. To obtain detailed information on how existing research has used The PopuList and to get an indication of its reach across different types of studies, we carried out a meta-analysis of 262 publications using data from Google Scholar. 4  Our analysis yielded the following results. First, 72. 5 per cent of materials using The PopuList are peer-reviewed journal articles. These studies range from single case studies to small-N and large-N comparisons. Second, The PopuList appears mostly in articles published in European and US comparative politics journals. Third, regarding the types of parties that are being classified using The PopuList, the most popular classification category is 'populism', with 116 items (44 per cent) using The PopuList to classify 'populist' parties only.
Next is the 'far right and populism' combination, with 68 references (26 per cent). This reflects the proliferation of studies on far-right populism in the past few years. Finally, 208 items in our analysis (79 per cent) use The PopuList in conjunction with other datasets, including the Chapel Hill Expert Survey (CHES), the European Social Survey (ESS), the Comparative Manifesto Project (MARPOR), and social media datasuggesting a broad focus ranging from demand-to supply-side perspectives (see Figure. 1 ).
The PopuList 3. 0 The new version of The PopuList includes an updated list of all elections until 31 December 2022. We formalized and systematized the EiQCC process described above and enhanced the database by adding country reports in which we discuss all the parties included in the list. Our party selection rationale has been as follows: we commenced by examining all parties that fulfilled our selection criteria in each country; that is, parties that have either won at least one seat or 2 per cent of the vote in national parliamentary elections since 1989. From this pool of parties, we selected those that may be classified as either populist, far left, or far right on the basis of their core ideological attributes. Whereas Euroscepticism was a primary coding category in The Populist 1. 0 and 2.
0, it was only a secondary coding category in The PopuList 3. 0. This means that in the latest version of the list, we have only examined Euroscepticism among those parties that we had already classified as either populist and/or far left/right. The reason for this decision was that the inclusion of all Eurosceptic parties resulted in the addition of many relatively moderate mainstream parties. This distracted from the main focus of The PopuList. That we included Euroscepticism as a secondary coding category only implies that scholars cannot use the database for a comprehensive list of Eurosceptic parties. As a next step, our classification was sent to country experts, who were also asked to identify any omissions or inconsistencies. The country experts' responses were subsequently reviewed and processed by the responsible PopuList team member (where necessary, the team member asked the experts for further clarification or scrutiny).
Although the former party was a populist party before the foundation of the PdL -Berlusconi often employed a populist rhetoric, making use of a simple and clear language that distinguished between the 'good', 'normal Italians' and the 'evil', 'arrogant' 'communist' political elite  (Rooduijn 2014 ). AN has never been populist  (Mudde 2007). Hence, because the PdL was a merger of a populist and a non-populist party, we have classified it as a borderline case. Whether a party falls within the populist or far left/right and Euroscepticism categories can, of course, change over time. Compared to previous versions, we have made a conscious effort to focus on such changes and made these explicit in our dataset and in the country reports. The PopuList very much aspires to be a dynamic project that responds to real-world changes in European politics and, therefore, leads to regular updates.
To identify how The PopuList compares to other datasets that include measures of constructs related to populism, the far left, and the far right, we carried out a comparison with POPPA data  (Meijers and Zaslove 2021). See supplementary material for details. We focus on POPPA because it includes the most fine-grained measure of populism to date for political parties. This comparison is useful because it highlights substantial overlap between the two databases. 5 This includes convergence on the classification of a number of West European populist parties that compete increasingly successfully within their respective party systems. One such example is the French National Rally (formerly National Front). We have classified this party as populist (and far right) because of its people-centric ideology whose focal point is a purported struggle for the 'real interests' of the French people against the French political establishment  (Rooduijn 2014).
Second, the comparison helps us to pay attention to outliers: (a) parties that are classified as populist/far left/far right in The PopuList but do not score high on the corresponding continuous measures in POPPA and (b) parties that are not classified as belonging to one of The PopuList's categories but score high on the related continuous measure in POPPA. We identify most discrepancies with respect to the first category: specifically, we identify ten parties that are classified as populist in The PopuList but score relatively low in POPPA. These cases consist mostly of The point-biserial correlation between our dichotomous classification of populist parties and POPPA's continuous measure is r = 0. 66, which indicates a good degree of overlap. The point-biserial correlation between our far right classification and a measure of far right ideology composed from the available POPPA data is r = 0.
68, which indicates a similar degree of overlap. The point-biserial correlation between our far left classification and the only variable in the POPPA dataset that could be used to tap into far-left ideology is relatively low (r = 0. 51). This is due to the fact that the only variable that could be employed to measure far-left ideology is far from ideal. See the supplementary material for more information. parties from Central and Eastern Europe that hold relatively moderate ideological positions and have often had government responsibilities. One such case is the Citizens for European Development of Bulgaria (GERB), a right-wing party that has been a central force in Bulgarian politics since the late 2000s. The low score in POPPA might well be due to the party's central role in the political system and its involvement in corruption scandals while in power.
Expert surveys are constrained in these respects, as they often only capture the position of parties at a given point in time (for example, elections or the administration of the survey). Of course, The PopuList is not without limitations. First, it is narrower than other databases such as POPPA and the Chapel Hill Expert Survey (CHES) because it does not provide information on parties that are not populist, far left, or far right. But this is not necessarily constraining for end-users given the complementarity with other data. As shown in our meta-analysis, most research that draws on The PopuList does so in conjunction with other datasets. Second, so far, we have relied on a limited set of experts that notify us regularly of changes and new parties. However, considering the proliferation of studies in the field, there is a growing pool of country experts that can further enhance knowledge of individual cases.
Moreover, given the deliberative nature of The PopuList, our classification is not dependent on specific experts. Third, while The PopuList is limited to Europe, we hope to extend the use of EiQCC and the scope of our dataset to other regions. Finally, version 3. 0 has improved on the previous release by focusing on a longer time periodthus including more partiesand by offering detailed country reports, which provide a discussion of borderline cases. Overall, we believe that the extensive use of earlier versions of The PopuList, as well as the frequent use in combination with sources of quantitative data, shows the demand for our dataset and the value of systematizing iterative and collaborative classification procedures like EiQCC. Supplementary material. The supplementary material for this article can be found at https://doi.
Acknowledgements. We would like to thank the  Guardian  (in particular  Paul Lewis ) and the  ECPR Standing Group on Extremism and Democracy  for their support during the early days of The PopuList. Andrea Pirro  would like to acknowledge support from the  European University Institute ; part of this work was carried out during his visiting fellowship at the  Robert Schuman Centre for Advanced Studies. Caterina Froio  would also like to thank the participants at a pre-publication seminar at  Sciences Po/CEE. This project would not have been possible without the help of our research assistants,  Philipp Mendoza ,  Martin Widdig , and  Hannes Bey. Finally , The PopuList is the product of a close cooperation with country experts. Therefore, we would like to extend our gratitude to all the scholars who have helped us with the classification of parties and the country reports.
Measurement of cytotoxicity: On the day prior to the stimulation, KERTr cells were seeded into 24-well plates (4 × 10 5 cells/well) in EGF-free culture medium. On the next day, solutions containing different concentrations of EHD (1-30 μM) were added for 24 h. Then, the cells were incubated with propidium iodide (PI; Invitrogen, Illkirch, France). Necrotic cells were stained by PI and were analyzed by flow cytometry using Attune NxT (ThermoFisher Scientific, Illkirch, France) and the FlowJo software (Becton, Dickinson & Company, Franklin Lakes, United States). Results were expressed as the percentage of living cells (PI -), and a concentration resulting in 70% cell viability of exposure (CV70) could be determined. Chemical exposure: The cells were exposed to CinA 100 μM, EHD 10 μM, and a mixture containing both compounds at the same concentration for different exposure times to study the NRF2 pathway and inflammation in KC.
As shown in Table  1 , specific forward and reverse primers were used at a final concentration of 0. 5 μM for the detection of the investigated genes HMOX1, NQO1, IL1A, IL1B, IL6, CXCL8, IL23A, IL24, TNF, TSLP, ACTB and GAPDH encoding, respectively, for HO-1, NQO1, IL-1α, IL-1β, IL-6, IL-8, IL-23 (subunit α), IL-24, TNF-α, TSLP, β-actin and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Each sample was monitored in duplicate. The results were analyzed using the Bio-Rad CFX Manager software and were expressed by fold factor calculated by comparing the Ct values obtained from treated and untreated cells (PBS 0. 1%) at the same time of exposure and corrected with ACTB and GAPDH expression. Statistical analysis: The Mann-Whitney U-test was used. All data are presented as mean values ±SEM. Measurements were considered significant at a p-value ≤ 0.
Results A CV70 of EHD equal to 10 μM in KERTr cells was determined by flow cytometry (data not shown) and was used in this work. For CinA, a value of CV70 equal to 100 μM had already been determined in previous work. Moreover, the ability of CinA 100 μM to induce NRF2 pathway had been shown in KERTr cells  (Vallion et al. , 2022). The effect of the mixture EHD plus CinA on cell viability in KERTr cells was also investigated and did not induce different cell viability after treatment compared to EHD or CinA alone (Supplementary Figure  S1 ). Study of NRF2 pathway in response to EHD and a mixture containing EHD and CinA KERTr cells were treated for 2 h or 6 h with EHD, CinA or a mixture of these two compounds. NRF2, HO-1 and NQO1 protein levels were measured by Western blot. Regarding EHD, no accumulation of NRF2 and a low expression of HO-1 (p ≤ 0.
, 2022. In KERTr Sh NRF2, the mixture also caused an increase in IL1A, IL6, CXCL8, IL23A and IL24 expressions after 2 and 6 h of treatment and in IL1B expression only after 6 h of treatment (Figure  3 ). Hence, it implies an inhibitory role of NRF2 on the expression of these genes involved in inflammation and then, on inflammatory response induced by the tested compounds. For TNF gene, there were lots of variabilities making any conclusion difficult (data not shown). After 2-h treatment, a significant decrease in IL1B and IL24 expression with the mixture compared to EHD was measured in both types of KERTr cells (Figure  3 ). Contrary to normal KERTr cells, no difference between the mixture and the compound alone was measured in KERTr Sh NRF2 cells for IL6 expression. For IL1B expression in KERTr Sh NRF2, after 6 h of treatment, a higher expression can be highlighted with the mixture than EHD or CinA alone.
Thermodynamic modeling The GEMS software  [25]  was used to predict the most thermodynamically stable phases to form from the reactions between hardened Portland cement and the various aqueous phosphate precursors. This software minimizes the Gibbs free energy of the entire system to reach thermodynamic equilibrium and predicts which phases should form as a function of the reaction parameters (e. g. pH or precursor concentration). In addition to the general PSI-Nagra database  [26] , the Cemdata18 database  [27]  was used to describe hydrated Portland cement together with thermodynamic data on magnesium and calcium phosphates  [28] [29] [30] [31] [32]  to predict the thermodynamically stable outcome of reacting aqueous phosphate solutions with cement. The formation of quartz, Al-containing siliceous hydrogarnet and magnesite was suppressed due to kinetic reasons.