dataesr/scientific-paragraphs-categorization huggingface dataset:
Constraints on grammar The Grambank dataset focuses on 195 core grammatical features (see table  S4 ). Even this basic set of features represents an astronomical number (>10 34 ) of possible grammars-the possible "design space" (sensu Dennett)  (29). How constrained is the distribution of the world's actual realized grammars within this total design space and what are the most important axes of variation? Some have claimed that languages are tightly constrained systems-"un système où tout se tient" [a system where everything fits together;  (30) ]. Many generative linguists assert that human cognition imposes strong constraints on grammatical variation such that only a small number of underlying factors are required to explain the observed diversity  (31) (32) (33). In contrast, others have argued that distinct components of language can vary individually, "All parts of a language appear in principle to be independently mobile"  (34).
Grambank's broad suite of logically independent traits (see the Supplementary Materials, SM1:1), systematically coded across a global sample of languages (see Materials and Methods), makes it an ideal resource for exploring these claims. We use principal components analysis (PCA) to reduce the dimensions of the Grambank data to a set of orthogonal variables representing the underlying patterns of variation among the grammatical features we consider (see Materials and Methods). A nongraphical Cattel's Scree test  (35)  showed that the optimal number of components is 19, which explain 49% of the variation among grammars. The first three components returned by the PCA capture only 21% of the variation (9, 7, and 5%, respectively). These results can be compared to similar studies of musical and genetic variation.
A recent analysis of cross-cultural musical behavior found that only three components optimally described the variation  (36). In contrast, an analysis of human genetic variation across Europe in the form of single-nucleotide polymorphisms found that the first and second principal components explained under 1% of the variation (0. 3 and 0. 15%, respectively)  (37). This indicates that language structures have greater combinatorial flexibility than musical behavior, but far less than genetic evolution. Grammatical systems are thus neither tightly constrained nor entirely free to vary. Having eliminated nearly all strict logical dependencies from our dataset (see Supplementary Materials, SM1:1), the sizable fraction of grammatical variation that is explained by a limited set of dimensions could reflect functional or historical constraints on grammar.
According to our metric, a language is more unusual than another if (i) some of its features and/or (ii) some of its combinations of features are more infrequent, comparatively speaking. It should be stressed that this operationalization of unusualness is necessarily restricted to the features present in Grambank; in other words, we make no claims about the unusualness of languages with respect to linguistic features not covered in the database. The global distribution of unusualness is richly structured (fig. S23 ). The most unusual languages are most often not members of the largest language families, or if they are, they are found at the geographic periphery of their expansion. In particular, several of the most unusual languages are isolates, i. e. languages with no known connection to any established language family [e.
DISCUSSION The Grambank data open up the possibility of quantitative crosslinguistic comparison on a scale that was not previously possible. This paper set out to answer four questions about global patterns of grammatical diversity with the Grambank data. What are the relative roles of genealogical inheritance and geographical diffusion in shaping grammatical diversity? How constrained is grammatical evolution? What are the world's most unusual languages, and what will the consequences of language loss be on our understanding of linguistic diversity? Our analyses revealed that, contrary to  widespread claims, genealogy had a dominant role in shaping patterns of grammatical diversity and the distribution of unusual languages. We found that the world's most unusual languages are often members of small language families or even isolates.
We anticipate that future work will use Grambank data to test more nuanced claims about language universals, linguistic areas, and the factors that drive the evolution of linguistic disparity. Grambank will also facilitate the exploration of links between linguistic diversity and a broad array of other cultural and biological traits, ranging from religious beliefs to economic behavior, musical traditions, and genetic lineages. We hope that these links with other facets of human behavior will help make Grambank a key resource not only in linguistics but also in the multidisciplinary endeavor of understanding human diversity.
Data Grambank is a large-scale database of language structures. The dataset is built on a questionnaire of 195 features. The Grambank dataset version 1. 0 contains 2467 language varieties, spanning all continents and major language families. For the analysis in this paper, we chose to remove all but one dialect per language, which leaves us with 2430 languages. The Grambank data gathering procedure progresses per language, i. e. , the entire questionnaire is filled in as much as possible for one language at a time based on published grammatical descriptions [typically sources classified as "grammar" or "grammar sketch" in Glottolog;  (1) ]. This procedure leads to high data coverage per language compared to, for example, the WALS  (8) , which is more akin to an anthology of features. Grambank contains 24% missing data, which can be compared to 84% in WALS (fig.
S1 ). The Grambank dataset features great coverage per continent and language family, and fig. S2  shows the Grambank coverage per Glottolog macroarea. For more information and background on the Grambank data project, see the Supplementary Materials, SM1:5. For the analyses in this paper, it was necessary to merge dialects, binarize features with multistate values, crop features and languages with large amounts of missing data, and/or impute the remainder of the missing data. There are 2467 language varieties in Grambank, including 70 dialects. To maximize the overlap with other data sources used in the analysis [e. g. , WALS  (8) , AUTOTYP  (45) , and the global tree (  26 )], we chose to drop all but one dialect per language. If two datasets have entries for two different dialects of the same language, they would not be matched to each other unless they were both reduced to their language level ID.
The dialect that was retainedwas the one with the least amount of missing data. The remaining language variety is assigned the glottocode of its parent language variety that is classified as "language" in Glottolog (i. e. , not "dialect"). For the comparison of coverage between WALS (8) and Grambank (see fig. S1 ), we also reduce dialects in WALS by keeping the one with the least amount of missing data in the same fashion. This leaves 2430 languages in Grambank and 2435 languages in WALS. There were 35 languoids in WALS that were not mapped to a glottocode and therefore not possible to include in the comparison at all. There are six features in the Grambank dataset that have multistate values; all others are binary. Multistate features all concern word order: "what is the order of elements X and Y?" with the alternatives "XY," "YX," or "both.
" They were all split into two features each, of the format "Is the order XY?" and "Is the order YX?" with the "both" values triggering a 1 (yes) for both features. This process gives 201 binary features of the original 195. The full dataset contains 24% missing data. To avoid problems of excessive imputation, we first crop the dataset such that we remove features and languages with more than 25% missing data, leaving 1509 languages and 113 binarized features. There remains 4% missing data in the cropped dataset. These missing data are imputed using a random forest trained on the observed values, as implemented in the R package "missForest" v. 1. 4  (49, 50). The out-of-bag error rate is estimated at 14%. The random forest technique is entirely naive as to language genealogy or geography; it imputes missing data based on languages with a similar profile regardless of relatedness or spatial distance.
For each analysis, we specify if the imputed dataset was used. We used a Maximum Clade Credibility tree from recent global language phylogeny  (26) , which contains over 6000 languages. As with the Grambank and WALS datasets, we reduced dialects to languages to increase overlap. Tips that are mapped to the same language were reduced to one by choosing one tip in the set at random. We also dropped tips in the global tree that did not correspond to languages in our pruned and imputed dataset. This left 1404 tips in the global tree and languages in the Grambank dataset for the spatiophylogenetic analysis. Information on descriptive status, endangerment status, dialect membership, longitude/latitude, language family, and macroareas were taken from Glottolog (1). To group languages into cultural areas for analysis and visualization, we used the classification provided by AUTOTYP  (45).
Analysis: Spatiophylogenetic modeling The estimation of spatial and phylogenetic effects for each feature of Grambank was calculated using a binomial spatiophylogenetic model following the procedure laid out in  (25). This model is implemented using Integrated Nested Laplace Approximations (INLAs) of a Bayesian model using the R package INLA v20. 03. 17  (51). We used the binarized, dialect-merged, and cropped version of the Grambank dataset, but not the imputed values. The model contains two structured random effects: one representing the phylogenetic relationships between languages, and one representing the spatial distances. A key departure from the procedure laid out in prior research  (25)  is that the spatial relationships are represented as spatial coordinates, unlike in the procedural paper where spatial relationships are represented within a spatial mesh.
We can only include languages from Grambank that are also represented in the phylogeny. There are 1404 languages that appear in both the dialectdropped, cropped, and binarized Grambank dataset (see the previous section) and the phylogeny. To maximize overlap, the global tree was also dropped for dialects (dropping all but one tip at random out of sets of tips that are dialects of the same language). The dataset used in this analysis contains 4% missing values, and we did not impute them. We followed the same principles for cropping for missing data as outlined in the previous section, leaving us with 113 features. The spatiophylogenetic model uses precision matrices to represent the phylogenetic and spatial relationships, which are calculated from covariance matrices. Phylogenetic covariance is estimated through a model of Brownian motion, and spatial covariance is determined through a Matérn covariance function.
Analysis: PCA We carried out a traditional nonweighted PCA to derive the dimensions along which data primarily varied. We used the function prcomp in R v4. 1. 0 (56). The data were dialect-merged, binarized, cropped, and imputed for the PCA. It is necessary that the data are binarized because the PCA relies on the mean of each variable, which is not meaningful for multistate features. It is also necessary to remove and/or impute missing data as PCA requires a complete dataset. The variables were scaled to have unit variance and centered. We examined the rotations/loadings of the components for each feature (fig. S15 ). To evaluate what phenomena most contributed to each component, we also examined the rotated data per language and compared to other aggregate scores capturing known linguistic theoretic concepts.
We compared the rotated data to concepts used in linguistic typology to characterize language variation. For each of these concepts, we created an index that measures, for each language, the occurrence of Grambank feature values that might be expected in a language that perfectly exemplifies the relevant theoretical properties. The concepts we encoded with typological indices are as follows: 1) Word order [the degree to which a language uses structures hypothesized to correlate with verb-object or object-verb word order in  (38, 39) ]. 2) Locus of marking [the degree to which a language mainly features head or dependent marking, as described by  (40) ]. 3) Fusion [degree to which a language encodes meanings and functions with bound morphology as opposed to phonologically free-standing markers  (41) ].
Analysis: Cultural fixation scores Fixation scores (often abbreviated F ST ) are a way of measuring similarity between groups of data in a dataset. It is commonly used in genetics to study how close different groups of individuals are and how the structure compares to what would have happened if everyone reproduced randomly. The outcome of the analysis is a score for each pairing of groups in your data. A low score indicates that members of those two groups are similar, whereas a high score indicates that they are dissimilar. The value is dependent on both the between-group and within-group variation in the data, as well as the overall frequency of the variable in the entire dataset. There are several different approaches to fixation scores in the literature. For this study, we used the method proposed by  (58) , which is developed specifically for cultural data.
For more on the details of the cultural fixation score and how it differs from other fixation scores, see  (58). For the Grambank dataset, we use the groups from the AUTOTYP project (25 cultural areas like "Andean" and "Indic") and the macroareas from Glottolog 4. 0. Each language is associated with one of these regions, and the pairwise cultural fixation scores indicate how likely it is that two areas should be merged or kept separate. This analysis uses the dialect merged, cropped, and binarized dataset (i. e. , 1509 languages and 113 features)-but not imputed data. To illustrate the scores, fig. S19  shows a barplot of cultural fixation scores over macroareas and fig. S20  shows the cultural fixation scores over AUTOTYP areas. To investigate whether the AUTOTYP areas that are found in the Americas do form a distinct cluster, we rendered a network based on the cultural fixation scores and computed the modularity score if we group the nodes into Americas versus not Americas (see fig.
Analysis: Unusualness We define unusualness based on the information-theoretic notion of surprisal. According to this measure, a language is considered to be more unusual the rarer its features and/or combinations of features are cross-linguistically. Concretely, we compute the surprisal associated with each language i U i ¼ logðP i Þ ð1Þ where P i ¼ PrðX 1 ¼ x 1 i ; X 2 ¼ x 2 i ;. ; X 113 ¼ x 113 i Þ ð2Þ is the probability of the Grambank description of language i. Estimating P i is complicated by the fact that our sample size is much smaller than the number of possible grammars (i. e. , what is referred to as a n<<p scenario in machine learning). We overcome this obstacle by constructing a model-based estimator based on different assumptions about the structure of grammars. We used the dialect-merged, binarized, cropped, and imputed version of the Grambank dataset for the unusualness analysis.
Probability density estimation For this analysis, we used the dialect-merged, cropped, imputed, and binarized dataset (see the Supplementary Materials), which contains 1509 languages over 113 features. The possibility space (the number of possible distinct languages in the Grambank description) is 2 113. However, our goal is to approximate the probability distribution of the Grambank description of the languages that exist today-and not some theoretical distribution of "possible" or "frequent" languages independent from the finite sample we were able to observe. In this regard, our sample is not negligible, specifically when contrasted with the number of languages for which a comprehensive grammar exists (~4000). Nevertheless, a direct estimation of the probability distribution is unfeasible as all Grambank descriptions are unique (and we do not want to assume that all languages not described in Grambank have to be identical to some other Grambank language).
Unusualness model We deploy a Bayesian regression model of unusualness. The spatial and phylogenetic effects are both variance covariance (VCV) matrices based on a Brownian motion approach. The spatial data are taken from Glottolog (1), and the phylogeny is the global language tree  (26). This is the same method of generating the VCVs as the INLA modeling, with the same kappa and sigma values (2 and 1. 15, respectively) for the spatial VCV. The rest of the analysis is different in that it uses BRMS rather than Bayesian inference for latent Gaussian models (INLA). We use default (uninformative) priors for all coefficients as implemented in the Stan wrapper brms R package  (60). We ran four independent chains for 6000 iterations, and all parameters of the model showed convergence quickly into the run of each chain.
Analysis: Manhattan distances Manhattan distances show the sum total of the number of differences between two records of data, in our case, between pairs of languages. For this metric, we used the binarized version of the dataset, i. e. , each language for each feature had a value of 0 or 1 (or missing). If there are 10 binary features, then a Manhattan distance of 4 for a pair of languages would mean that, for four features, they had different values (0 when the other had 1 or vice versa). This measurement is not relative to how many complete pairs of data points there are. If for one feature and one language pair, there is at least one missing data point, that feature is ignored. A Manhattan distance of 0 means for all features the language pair has exactly the same values. For the calculation in our dataset, we used the dialect-merged and cropped, but not imputed, data.
There are 113 features in the dataset that is cropped for missing data, meaning that the maximal possible Manhattan distance between any two languages is 113. The highest value found was 74; the pair consisted of the Sino-Tibetan language Wambule [wamb1257] and the Atlantic-Congo language Bobangi [bang1354]. There were six language pairs with a distance of 0. In each of these cases, the two languages were from the same language family (see table  S10 ). The mean distance was 39. A plot of the distribution is found in fig. S26.
Analysis: Functional richness We followed the approach used in ecology where functional richness analyses are commonly based on principal coordinates analysis (also known as classical metric multidimensional scaling), as this maximizes the amount of the total variation in the dataset that can be captured in two dimensions (here, 33%). We calculate this using the R package fundiversity  (62). The analysis is carried out groupwise within each language area and at a global level. Functional richness is calculated twice, once with all languages in the region and once with endangered languages removed. We use the Agglomerated Endangerment Scale (AES)  (63)  and categorize languages as either nonthreatened or threatened (the latter of which includes all AES categories associated with endangerment or recent dormancy).
Parents with better-educated children are healthier and live longer, but whether there is a causal effect of children's education on their parents' health and longevity is still unclear. First, we demonstrate an association between adults' offspring education and parental mortality in the 1958 British birth cohort study, which remains substantial-about two additional years of life-even when only comparing parents with similar socioeconomic status. Second, we use the 1972 educational reform in England and Wales, which increased the minimum school-leaving age from 15 to 16 years, to identify the presence of a causal effect of children's education on parental health and longevity using census-linked data from the Office for National Statistics Longitudinal Study. Results reveal that children's education has no causal effects on a wide range of parental mortality and.
, 2020a) , have better cognitive functioning  Ma et al. (2021) ;  Yahirun et al. (2020b) , suffer less from functional limitations  (Yahirun et al. ,  Research Agency (ANR), 'Investissements d'Avenir' [LabEx Ecodec/ANR-11-LABX-0047]. The permission of the Office for National Statistics to use the Longitudinal Study is gratefully acknowledged, as is the help provided by staff of the Center for Longitudinal Study Information and User Support (CELSIUS). CELSIUS is supported by the ESRC Census of Population Program under project ES/K000365/1. The authors alone are responsible for the interpretation of the data. ONS agrees that the figures and descriptions of results may be published. This does not imply ONS's acceptance of the validity of the methods used to obtain these figures, or of any analysis of the results.
This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. The permission of Paul Norman to use the 2011 Carstairs index of deprivation he created 2 2017;  Zimmer et al. , 2002) , less from inflammation  (Lee, 2018) , and live longer  (Elo et al. , 2018; Friedman and Mare, 2014; Jiang, 2019; Sabater and Graham, 2016a; Torssander, 2013 Torssander, , 2014;; Wolfe et al. , 2018a,b; Yahirun et al. , 2017; Yang et al. , 2016). This has become known as the 'social foreground' hypothesis  (Torssander, 2013). The evidence so far suggests there might be health benefits to children's education that extend beyond their own lives, and that investing in education is an effective strategy for improving the living conditions of a wider range of people.
In the second empirical analysis, we exploit an educational reform affecting the birth cohorts from 1957 onwards and linked census data  (Shelton et al. , 2019)  to assess whether the association between adult children's education and parental health can be considered causal. These two analyses exploit individual strengths of the two datasets and complement each other. The 1958 cohort study can be used to show the association between educational attainment and parental life expectancy, yet it does not allow ruling out crucial confounding factors using a quasi-experimental design. The census data allow us to use a quasi-experimental design as children vary in their birth dates around the time of an educational reform, but does not provide information on children's educational attainment necessary to establish association 1.
e. leaving school at age 21). This difference is similar to predictions in previous studies reporting about two years difference in life expectancy  (Friedman and Mare, 2014). Finally, several robustness checks were carried out, reported in Table  A4  and Table A5. First, we assessed the role of missing values by comparing the results to those obtained from using multiple imputation with chained equations (MICE,  White et al. , 2011). Second, we restricted the sample restricted to birth cohort members who lived in England and Wales at age 16 (excluding those living in Scotland) in order to make it comparable to the data used in Empirical Analysis 2. Third, we included an interaction of children's education with time. Fourth, we use an accelerated failure model with survival time following a Weibull distribution.