dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. C. P. passed away during the time we were working on the manuscript; we are grateful for his valuable contributions to this research. We thank  Daniel Balliet  for his helpful comments on an earlier draft of this manuscript. Research was supported in part by the  Dutch Research Council (NWO)  under Grant No. 022. 003. 040 ,awarded to P. A. M. -V. L. The contributions of M. H. and S. Graf were supported by Grant No. 20-01214S  from the  Czech Science Foundation  and by  Rozvoj Výzkumné Organizace (Development of a Research Organization) :  RVO 68081740  of the  Institute of Psychology, Czech Academy of Sciences. The contribution of R. G. was supported by the  Centre for Social Conflict and Cohesion Studies  ( ANID/ FONDAL 15130009 ) and the  Center for Intercultural and Indigenous Research  ( ANID/FONDAP 15110006 ).
GDP/GNI Gross Domestic Product/Gross National Income. World Bank (US2005 constant), values 2014, 2015; http://data. worldbank. org. Gini Coefficient of income inequality. Religiosity "Important in life: Religion. " World Value Survey (wave 6); European Values Study. Rule of law "The restriction of the arbitrary exercise of power by subordinating it to well-defined and established laws" (New Oxford American Dictionary). httpshttp://databank. worldbank. org/data/reports. aspx?source=worldwidegovernance-indicators&preview=on# Trust "Most people can be trusted. " World Value Survey (wave 6); European Values Study.
Data Collection The data presented here were collected through semistructured interviews facilitated by photo elicitation. Social and demographic information came from a preliminary recruitment interview. At that time, we also gave the girls the following instructions: "We want you to take a photograph of the table after a family meal. It should be taken before the table is cleared. There should not be anyone visible in the photograph, which means you should take it after the people at the meal have left the table. You can take as many pictures as you like, but in the end, you will choose just one to comment on with the researcher in the interview. " -How did you feel in these family meals?.
Review Data for this review were identified by searches of PubMed, Google Scholar, targeted websites about PPI, reports, and existing PPI guidelines, as well as Google Search and references from relevant studies. We used the following search terms: "patient and public participation," "patient engagement," "patient involvement," "consumer involvement," "community involvement," "participatory health research," "community based research," "research ideas," "co-writing," "coproduction," "co-design," "cohort study," "e-cohort," and "longitudinal study. " We included original studies describing PPI methods using the Involve definition with at least one type of PPI in the study. We included information on studies published in English from 2000 to the present.
Review of PPI Methods We analyzed data from 97 studies published from 2000 to 2020. The studies were performed in 9 countries: the United Kingdom (58/97, 60%), Canada (13/97, 13%), the United States (8/97, 8%), Australia (6/97, 6%), Ireland (6/97, 6%), Denmark (2/97, 2%), France (1/97, 1%), Portugal (1/97, 1%), Indonesia (1/97, 1%), and the United Kingdom and Australia (1/97, 1%). The most frequent methods of PPI were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). Of the 97 studies, 34 (35%) used only 1 method, whereas 30 (31%), 22 (23%), 8 (8%), and 3 (3%) used 2, 3, 4, and 5 methods, respectively.
It should be noted, however, that RWA will not be studied as an experimentally manipulated variable here. In this series of experiments, we shall simply try to manipulate the acquisition and change of attitudes. We start by introducing RWA and its relation to attitudes and by considering evaluative conditioning as a controlled attitude learning procedure. We then report and present two integrative data analyses conducted on data from 11 experiments that shed light on our research question, namely how attitudes are acquired and how they resist counter-attitudinal information as a function of RWA.
In two experiments, RWA and evaluative ratings were collected immediately after the conditioning phase (meaning that there was no counter-conditioning phase in these experiments). Here, we report and discuss two separate integrative data analyses on these two sets of experiments. These integrative data analyses have two main goals: to test whether RWA is related to more resistant attitudes in a counter-conditioning procedure (integra tive data analysis 1), and to test whether this effect is due to differences in RWA present in acquisition attitudes in a conditioning procedure (integrative data analysis 2). First of all, we hypothesized that RWA would be positively related to a resistance to novel counter-attitudinal information. This should result in a final evaluation that is more consistent with the conditioning than the counter-conditioning phase.
5 or positive = +0. 5) and RWA (observed variable initially bounded between 7 and 70 and with precision 1 and then transformed into a z-score), as well as their interaction as fixed factors and varying intercepts for participants and stimuli in the model. The dependent variable is the evaluation of Greebles on the ordinal scale from 1 (very negative) to 9 (very positive)  (Liddell & Kruschke, 2018). Integrative data analysis was performed on the data of all experiments: Experiments 1 and 11 for integrative data analysis 1 and Experiments 1 through 10 for integrative data analysis 2 (see Table  1  for an overview). The same parameters were kept for the integrative data analyses and a varying intercept for experiments was added. Before running the models, and before centering and scaling variables, we checked for the presence of possible extreme RWA scores in our sample (possibly due to measurement errors including -for instance-misunderstanding the questions or disengaging from the task).
Procedure Categorization stage: This step was similar to the one presented in integrative data analysis 1. Conditioning stage: The conditioning phase, similar to that reported for integrative data analysis 1, was immediately followed by the CS evaluation. Evaluative stage and scale completion: Participants were asked to state their attitude towards each individual Greeble (from 1 = "very negative" to 9 = "very positive"). CSs were presented in a random order. Finally, the participants completed the RWA scale, were thanked, debriefed and dismissed.
Statistics and Results The rationale underlying the data analysis was the same as for integrative data analysis 1. We also checked for the presence of possible extreme RWA scores in our sample. We excluded two participants over these two experiments (see Table  1  for an overview of all the experiments). The interaction between the initial valence and RWA has a probability of 100% of being positive (Median = 0. 22, 95% HDI [0. 12, 0. 33], see Figure  4  and Figure  5. 1). It is uncertain whether this effect is statistically significant. Even though only 4. 05% of the parameter values lie in the region of practical equivalence, the 95% HDI slightly overlaps with it. This effect can be considered to be very small  (Sawilowsky, 2009)  according to standard benchmarks 4 (pseudo-R 2 =. 003 ≈ d = 0.
Indeed, the meta-analysis carried-out by  Van Hiel et al. (2016)  indicates that behavioral measures of cognitive style usually yield smaller effect sizes than self-reported measures when addressing the question of right-wing attitudes (see also  Zmigrod, 2020 , for a more recent overview). Further studies comparing social vs. non-social tasks and laboratory vs. field experiments could inform us about the minimum practically significant effect size in this field of research. Our integrative data analysis shows the number of observations needed to detect effect sizes of d ≈ 0. 1 (and null effects corresponding to d < 0. 07) in this context  (Lakens, 2017 (Lakens, , 2021;; Lakens et al. , 2018). A third (complementary and not exclusive) possible way of accounting for asymmet rical valence effect in both the conditioning and counter-conditioning designs is to consider that some CSs were evaluated more positively even before the beginning of the 6) The same database we used in the experiments presented here.
Data collection and sampling Data collection was focused on four SM channels that each have a different communication focus. While YouTube is mainly based on videos, WhatsApp on chat texts, Instagram on photographs, Facebook is leveraging a blend of all of these media. The selection of SM channels followed two criteria. First, the channels had to be frequently used by NPOs. According to  Wilde (2015) , the four SM channels meet this criterion. Second, all SM channels had to be observable in one case company as a matter of comparability. The four SM channels selected for this study were all used by147 Rat auf Draht. Data were collected through a web questionnaire. The online survey included multiple-item questions measuring brand trust, process quality, outcome quality, and perceived SM performance (Table  1 ).
Analysis of moderating effects Analyzing the effects of the performance of various SM channels on the relationships between brand trust, process quality, and outcome quality reveals mixed effects. Facebook and YouTube have significant negative moderating effects on the relationship between brand trust and process quality, and YouTube has a slightly weaker effect. Both moderating relationships show a strong effect (Facebook -0. 424 versus YouTube -0. 522). Hence, the data support Hypotheses H4a and H7a whereas H5a and H6a are rejected in the current study. In other words, a weaker performance on brand trust can be compensated by a stronger performance in Facebook and YouTube activities. This is an interesting finding that will be discussed in more detail in the discussion section. The moderating effect of SM performance and brand trust on outcome quality is only significant on a 10%-level considering the activities on Instagram (std.
From a theoretical standpoint, it might also be beneficial to blend marketing theory with theoretical concepts of technology acceptance. Given the findings of our study, we would expect that customers' general acceptance of the technology that enables SM frames their perception of the services provided via the different SM communication channels. Finally, we would like to point out that the data for this study were collected before the Covid-19 outbreak. We assume that the identified patterns would be even more pronounced during the current pandemic. Overall, this manuscript shows that the general model of service quality still applies in the era of SM; however, different SM channels have different impacts, which is a major takeaway. Alongside answering our research questions this manuscript created insights that should be followed up in future studies.
There have been important studies of recent income inequality and of poverty in South Africa, but very little is known about the long-run trends over time. There is speculation about the extent of inequality when the Union of South Africa was formed in 1910, but no hard evidence. In this paper, we provide evidence that is partial-being confined to top incomes-but which for the first time shows how the income distribution changed on a (near) annual basis from 1913 onwards. We present estimates of the shares in total income of groups such as the top 1% and the top 0. 1%, covering the period from colonial times to the twenty-first century. For a number of years during the apartheid period, we have data classified by race. The estimates for recent years bear out the picture of South Africa as a highly unequal country, but allow this to be placed in historical and international context.
Introduction Income inequality in South Africa has received much attention. Over the last years, there have been important studies of recent inequality and poverty, and a heated debate about trends in post-apartheid transition. 1  South Africa has long been regarded as having one of the most unequal societies in the world. Consistent with this view, the country has the highest survey-based Gini coefficient of household consumption per capita in Povcal database  (63. 4 in 2011; 63 in 2014). In this paper, we approach the subject from a different direction: the extent and evolution of top incomes. We present estimates of the shares in total income of groups such as the top 1% and the top 0. 1%, covering, with gaps, more than a hundred years. As in other countries, top incomes are difficult to measure with precision.
They are often not well covered by the household surveys that are today the primary source of evidence about the distribution of income. A partial picture can, however, be obtained from the information contained in the income tax returns, and these are the sources employed in this paper. In this field, and in the related area of national income totals, South African researchers were among the pioneers. Leslie (1935 Leslie ( , 1936 Leslie ( , 1937) )  used income tax data to examine the effect on the South African distribution of income of the abandonment of the Gold Standard by Britain in 1931. Frankel and Herzfeld (1943)  published estimates of the income distribution among "Europeans" in South Africa based on the income tax returns, by making use of control totals from the census of population and from the national accounts; their use of external information to complement income tax data pre-dated by ten years the study of upper income groups in the US by  Kuznets (1953).
Graaff (1946)  assembled a series based on South African Super Tax data covering the years 1915 to 1942 to examine the stability of the distribution and the causes of fluctuations in income concentration. In seeking to exploit the (more than a) century of income tax data now available, we are therefore following in a long-established research tradition. The picture obtained from tax data is only a partial one because not everyone has to provide income information to the tax authorities, and in earlier years, the tax-paying population was a small minority of the total population; they were the better-off and, in the case of South Africa, very largely white. The picture is also partial in that the income recorded, gross income assessed for tax purposes, does not necessarily capture the full extent of the economic advantage accruing to those at the top of the distribution, and certain categories of income, notably dividends, are incompletely covered.
Our estimates of top incomes allow us to examine whether that has always been the case. The conclusion of  Graaff (1946)  was that the degree of income concentration (derived from the Pareto coefficient) was "fairly stable" over the long period. But now, we have many more years of data. Did this stability, however, remain in the apartheid years? Or was there a long-run trend in top income shares? In a recent article on inequality, van der Berg asked "what was the case in South Africa over the past century?" and went on to say that "no data exist to give a definitive answer" (2011, page 125). Our estimates are not definitive, but they provide a point of departure for those seeking to understand the long-run pattern of income inequality in South Africa. Many readers will want to go first to the results.
However, an appreciation of the methods used to arrive at the estimated top income shares is necessary to give due weight to their limitations. We therefore begin in Sect. 2 with a description of the income tax data: one of the contributions of this paper is the reconstruction of all the distributional data published by official sources on the basis of the income tax in South Africa since 1903. As already explained, the tax data cannot be employed on their own. The published distributions of taxpayers by income ranges have to be accompanied by external control totals for the total adult population and for total household income, and these are described in Sect. 3. The results for top income shares in South Africa from 1903 to 2007 are set out in Sect. 4, where we consider the changing shape of the upper tail.
The findings for South Africa are set in international context in Sect. 5, where we make comparisons with the findings for Australia, Canada, New Zealand, the UK and with other former colonies of the British Empire. In seeking to understand to understand the evolution of top income shares, we preliminary explore three determinants: conquest, discrimination and development. The main conclusions are summarised at the end. 2 Where do the estimates come from? The basic sources used in this paper are the tables published by the income tax authorities for the Cape Colony  (data for 1903-1907)  and the Union of South Africa (data from 1913). The Union was formed as a British Dominion in May 1910 from the former colonies of Cape of Good Hope, Natal, Orange River Colony (or Free State) and Transvaal.
The statistics for the former cover a larger proportion of the population (some 58,000 taxpayers in 1916, compared with fewer than 2000 Super Tax payers), but the Normal tax statistics exclude dividend income, a point discussed further below. In later years, information was published in South African Statistics, which appeared biennially from 1968. In 2009, the National Treasury and the South African Revenue Service began a new publication entitled 2008 Tax Statistics, containing information for 2002 to 2005, and which has appeared regularly since then  (National Treasury and South African Revenue Service 2009). The data employed here are not in the form of individual tax records, which no longer exist for most of the period studied; rather, we make use of published tabulations. The information necessary for the estimation of top income shares is the distribution of taxpayers assessed by ranges of income and, ideally (present in many, but not all, years) the amount of income in each range.
Under the Normal Tax/ Super Tax regime, dividend income was not assessed under the Normal Tax but under the Super Tax. A separate Dividend tax was levied (with higher rates for companies engaged in gold and diamond mining). The Super Tax data are therefore more complete, and for this reason have been used in earlier studies such as  Graaff (1946). However, they cover a smaller fraction of the upper incomes. The estimates prior to the 1940s are limited to the share of the top 0. 05%, whereas using the Normal Tax data we are able to estimate the share of the top 1%. 4 In view of this, we give two series: series excluding dividends (Appendix Table  5 ) based on Normal Tax data, up to 1953, and series including dividends (Appendix Table  6 ) based on the Super Tax data. Following the abolition of Super Tax in 1959, the latter is continued using the Personal Income Tax data, which included dividend income to varying degrees.
Control total for total income The tax records only cover a part of total household income. One of the major contributions of Kuznets' study Shares of upper income groups in incomes and savings  (1953)  was to combine income tax data with national accounts estimates of total income. However, he was not the first, having been preceded by South African economists Frankel and Herzfeld, who made estimates of the "European" income distribution in South Africa in 1943. Drawing attention to the limited coverage of the tax return data on their own, these authors argued that "by combining the national income and income tax statistics … it is possible to obtain a more general picture"  (1943, pp. 121-2). The national income estimates provide our starting point here. Our aim is to compare the incomes recorded in the tax returns with the total of household income after transfers but before tax as recorded in the national accounts.
There was also the move from a tax unit to an individual basis for taxation. Taking the post-2002 figures on their own, we can see that top income shares have increased, but we must stress again that the comparability of data (and, by extension, of the level of shares) in the 21th century with the years before 1993 is not granted. 7 Yet, the recent figures bear out the picture of South Africa as a highly unequal country.
5}/log 10 {2}. In Fig. 3 , this inverted Pareto-Lorenz coefficient is plotted for these shares, and using the share of the top 0. 05% in the income of the top 0. 5%. Since the distribution is only approximately Pareto in form, these coefficients do not coincide, but it may be seen that they move closely together. A number of early researchers examined the fit to the South African data of the Pareto distribution. Leslie (1935, p. 279)  found values for the inverted Pareto-Lorenz coefficient smaller than those found in European countries, suggesting less inequality at the top in South Africa. He reports a wide range, but our estimates suggest that the coefficient was between 2 and 2. 5 from 1913 until after the Second World War. The coefficient then decreased, starting at the end of 1940s, indicating less inequality among those at the top of the distribution.
Differing colonial legacy? Our data on top incomes have the advantage of covering virtually the entire period since South Africa became, when the Union was formed, a self-governing dominion, and increasingly acquired further political powers, culminating in full independence. In this regard, its initial political history was similar to that of Australia, Canada and New Zealand, and it is therefore useful to draw a parallel. How far is their current distribution a reflection of the colonial past? Did South Africa have a different colonial legacy? In considering this question, a potentially important role is played by the differing sizes of the indigenous population: this is the subject of the next sub-section. In Fig. 4 , we compare the findings for the share of the top 1% cent in South Africa with those for the three other dominions and for the United Kingdom, the former colonial power, for the period for which we can provide a racial decomposition with the white population.
Final remarks The income tax publications offer a rich store of historical data about the evolution of top incomes in South Africa. Together with estimates for the earlier Cape Colony, the series span more than a hundred years. The construction of the estimates has been described at some length in order to underline their limitations, which mean that there are several potential sources of error. Nonetheless, they provide a basis for placing the recent data on inequality in its long-run historical context and furnish evidence about distributional change in earlier periods. Our estimates track the evolution of top incomes over a long run of years, including the first half of the century when real incomes grew and the later decades that led to the collapse of apartheid. Top income shares were not stable.
There were shortrun movements and long-term trends. The share of the top 1% was halved between 1914 and 1993. The degree of concentration within the top 1% declined: people at the entry point in 1914 saw those above as having on average twice their income, whereas in the early 1990s the advantage was only some 1½ times. The income tax data for 1956 to 1987 allow us to examine the racial composition of the top income groups. These were, unsurprisingly, overwhelmingly white, and the degree of dominance was little reduced. At the same time, the non-white groups increased their representation (in the case of Africans by a factor of 120), and this shows that some mobility took place during the apartheid years. How far was South Africa different? We have compared top income shares in South Africa with three other former dominions: Australia, Canada and New Zealand, as well as with the UK.