dataesr/scientific-paragraphs-categorization huggingface dataset:
EMPIRICAL ANALYSIS 2: LINKED CENSUS DATA Data: The ONS Longitudinal Study The previous section has established the presence of associational findings while this second part of the analysis aims at determining whether there is a causal effect. We analyze the Office for National Statistics Longitudinal Study (ONS-LS,  Shelton et al. , 2019) , which combines linked census and life events data for a one-per cent sample of the population of England and Wales. The ONS-LS links records at each census since 1971 for people born on one of four selected (non-disclosed) dates in a year. Census information of the ONS-LS members is linked to life events data, including births, deaths, and cancer registrations. In 1971, women under the age of 60 who were married, widowed, or divorced were prompted to list the birth months and years of all live-born children who were born in wedlock.
Identification strategy Identification is achieved through a regression discontinuity (RD) approach (Imbens and Lemieux, 2008;  Lee and Lemieux, 2010). In particular, RD designs have four main features: the running variable, the treatment, the cut-off, and the bandwidth. The running variable is the children's date of birth, measured in quarter of birth (due to confidentiality restrictions imposed by the data provider), that leads to the assignment of the treatment. Second, the treatment is the increase in years of compulsory education for individuals born after September 1, 1957. The cut-off represents the quarter of birth after which the treatment is assigned. Therefore, the probability of treatment assignment based on the running variable has a discontinuous change after the reform implementation.
Finally, the accuracy of estimation depends on the bandwidth, i. e. the number of observations right before and after the reform threshold. It is crucial that the bandwidth estimation procedure is conducted in a data-driven way, such that the trade-off between bias (i. e. when the bandwidth is large, the larger is the bias) and variance (i. e. when the bandwidth is small, the larger is the variance) is minimized. Our design is a 'fuzzy' (as opposed to 'sharp') regression discontinuity approach because the reform increases the probability to stay in education until age 16 but does not solely determine whether a student stays in school. All the analyses are carried out with the Stata package 'rdrobust'  (Calonico et al. , 2017). We use a data-driven bandwidth estimation procedure (using the same mean square error optimal bandwidth on both sides) and check the sensitivity of the results to different bandwidths and bandwidths' selection algorithm.
Our study is not free from limitations. First, while it would be interesting to study additive effects of children education on parental health, due to the very reduced number of multiple births in the dataset this analysis could not been carried out. Second, the absence of a causal effect has to be understood locally and therefore, we cannot exclude the presence of an effect at other points in the educational distribution. Third, the data in hand did not allow us to make direct tests of potential causal mechanisms linking adult children's education and parental longevity, such as learning of improved health behaviors, increased material resources and reduced stress. Finally, parents in the first NCDS analysis are known to have some contact with their children (who respond to the survey at age 45), the second study uses ONS-LS census-linked data which does not assure any direct contact with the children after the birth of child.
A Replication materials and data references Analyses shown in this manuscript are fully replicable. Program code for the replication is available on-line  (Potente et al. , 2022). Data used in the study are publicly available. The birth cohort data analyzed in this study  (Power and Elliott, 2006)  as well as the data used for Figure 2  (Mindell et al. , 2012)  are available from the UK Data Service to registered researchers. The linked census data analyzed in this study  (Shelton et al. , 2019)  are available from the Office of National Statistics (ONS) to accredited researchers after the opening of an approved research project. Further information on how to obtain accredited researcher status is available from the Office of National Statistics. NCDS  (Power and Elliott, 2006)   B Question wording for longstanding illness and poor selfrated health in ONS-LS The census question wording for longstanding illness and poor self-rated health has varied over time, in Table  A1  we show the exact wording of the question and response options.
For determining m, the number of imputations, we made use of Von  Hippel's (2020)  algorithm, which suggested between 3 and 37 imputations for our models. We used 37 imputations for every model. We ran models on the imputed data and combined the resulting estimators using the rules stated by  Rubin (1987, ch. 3 ). Second, we restricted the sample restricted to birth cohort members who were lived in England and Wales at age 16 (excluding those living in Scotland) in order to make it comparable to the second part of the analysis which focuses on a schooling reform that only took place in England and Wales. In all cases, the magnitude of the coefficients changes slightly but the direction of the associations stays the same. Third, we assessed the proportional hazard assumption using Schoenfeld residuals, which suggested that the assumption might be violated for the adult children's education variable.
From Tables  A12  and A13  we observe that our analyses are able to detect smaller effects such as T = 0. 5 (0. 25σ) and T = 0. 8 (0. 4σ) with a high level of confidence. Only with T = 0. 2 (0. 1σ) we observe that some outcome could be slightly underpowered, but this represents extremely small effects. All the power analyses are implemented in Stata using the package 'rdpower'  (Cattaneo et al. , 2019).
The authors thank discussants at these occasions as well as  Rachel Stuchbury  and  Wei Xun  for invaluable help. This study has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement no. 681546  (FAMSIZEMATTERS). Patrick Präg's work was supported by the French National 1 health outcomes. We interpret these findings discussing the role of universal healthcare and education for socio-economic inequality in Great Britain.
The USA was the first state actor to release a defencespecific AI strategy, with the DoD publishing the strategy's executive summary publicly in February 2019  [8, 9]. This was followed in 2020 by the DoD adoption of five 'AI Principles', which outline that deployed AI must be: responsible, equitable, traceable, reliable and governable. 3  These principles apply "to all DoD AI capabilities, of any scale, including AI-enabled autonomous systems" as confirmed via a Biden administration memorandum  [40]. While the memorandum confirmed the focal point for the development of responsible AI infrastructure would be the Joint Artificial Intelligence Center (JAIC), from 1 February 2022, the office of the newly established Chief Data and AI Officer has taken on these responsibilities. While there is no all-encompassing national guidance regarding LAWS, the US Directive 3000.
Data collection and variables The reference population for this study is Ecuadorian habitants. Participants were invited to fill up a survey concerning COVID-19 impact on their mental health. Data collection took place between April and May 2020, exactly at the time of the mandatory lockdowns taking place. In this context governmental authorities ordered mobility restrictions as well as social distancing measures. We conduct three waves of social media invitations to participate in the study. Invitations were sent using the institutional accounts of the universities the authors of this study are affiliated. At the end, we received 2,403 answers, 50. 5% females and 49. 5% males. 49% of them have college degrees.
The academic study of grammatical voice (e. g. , active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large-scale text data, PassivePy, a Python package (readymade website: https://passivepy. streamlit. app/). This package achieves 98% agreement with human coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving.
g. , psychological distance) and behavioral intentions (e. g. , complaint escalation), but its identification is generally difficult without human coding or human interpretation. Existing computational tools to detect passive voice often consist of opaque algorithms that are untested in the wild, relying on proprietary software, or requiring extensive computational skills with a costly barrier-to-entry for most scientists. To address this need, we offer a transparent, flexible, and replicable Python package called PassivePy as well as a readymade website (https://passivepy. streamlit. app/), which meets and often exceeds human judgment accuracy to detect passive voice in large text collections. This work explicates the need for passive voice identification, the relevance of passive voice to consumer psychology, and how PassivePy can facilitate valid and reliable research into the psychological and behavioral correlates of grammatical voice at scale.
Although we specifically focus on how this tool can be used in consumer psychology and psychological science research, we expect that many fields will find our open and transparent package flexible for their needs. Those outside of academia (e. g. , practitioners and managers) can also use this tool to examine the text of reviews or complaints from their customers. As we show in this paper, passive voice is most prevalent when consumers are narrating a negative experience, and this could be a marker of perceived incident severity or further complaint escalation tendencies. Therefore, in just one example, managers can use our tool to examine passive voice as a marker of these psychological and behavioral dynamics. In the sections that follow, we first present a primer on text analysis research and where passive voice fits within the broader domain of research on text analysis.
Importantly, people often communicate about negative experiences to voice their displeasure (e. g. , poor service interactions with a company, a medical problem for which an individual needs to raise money) and potentially distance themselves from that event. Consistent with the established link between negative life events, psychological distance, and passive voice, we benchmark the utility of examining passive voice in negative consumer contexts by evaluating base-rate occurrences of this linguistic feature. As summarized in Table  2 , we collected text data from four datasets within three contexts relevant to consumer psychology, and where negative consumer experiences may be reported (e. g. , consumer complaints, consumer reviews, charitable-giving). The evidence suggests that, on average, passive voice is more prevalent in events of customer dissatisfaction (e.
g. , consumer complaints, product safety incidents) than on social media or charitable giving. To provide further support for this conclusion, we further examined prevalence of passive voice across various star levels in the Yelp dataset. Consistently, we find that average percentage of passive voice sentences is higher in negative reviews (M1-star = 11%, M2-star = 9%) than in positive reviews (M4-star = 6%, M5-star = 5%). Therefore, negative service interactions and unfavorable consumption experiences might be fruitful domains for examining passive voice and its links to consumer outcomes, though there are others that deserve consideration in future studies as well (e. g. , promotional content such as advertising and social media, plus product content such as books and movie scripts). We use the prior evidence as support for explicating who may benefit from a tool that can evaluate passive voice in consequential consumer contexts of interest.
Use Case 1: Psychological Distance Research As our prior review suggests, the first use case could be for researchers examining psychological distance and its downstream consequences on behavior. Given that passive voice increases one's psychological distance from an experience  (Bohner, 2001; Fausey & Boroditsky, 2010; Henley et al. , 1995; Lamb, 1991) , the use of passive voice by a complainer in a consumer context might indicate that they are attempting to distance themselves from a problem and minimize their potential role in the issue. Therefore, the use of passive voice in a complaint could be an indicator of less self-attribution and more company-attribution of a fault. The CFPB complaints dataset. To demonstrate the utility of this approach, we collected data from the Consumer Financial Protection Bureau (CFPB).
This dataset includes the complaints submitted to this agency, the categorical response from the company, and whether the complainer decided to escalate the situation or accept the company's response (for an application of this data see  Jung et al. , 2017). Using the tool introduced in this paper, PassivePy, we examined use of passive voice among consumers who disputed (vs. accepted) a company's offered resolution in a dataset of 164,064 complaints submitted to CFPB between March 2015 and April 2017. We find that using more passive voice in complaint narratives was associated with higher likelihood of disputing (vs. accepting) a company's offer (statistical details in Web Appendix A1). While the point of this analysis is to demonstrate the prevalence and potential of analyzing passive voice in consumer psychology contexts, future research could also examine potential underlying mechanisms for such effects and examine what passive voice reflects about writers' psychological processing and their behavioral intentions.
Furthermore, past research in psycholinguistics has evaluated linguistic measures of psychological distance  (Nook et al. , 2022). Future research in this domain could examine the relationship between these distance measures and passive voice. Based on previous findings in this domain (e. g. ,  Chan & Maglio, 2020) , we expect a positive relationship between use of passive voice and linguistic measures of psychological distance. Such examination would provide further support for experimental findings on the association between passive voice, construal, and psychological distance. As a preliminary test, we examined the correlation between distancing language and passive voice in the same sample of 164,064 consumer complaints described above. We used the approach introduced by Nook et al. (  2022 ) to compute the distancing language measure and PassivePy for computing passive voice.
We found a positive association between distancing language and the use of passive voice (statistical details in Web Appendices A2 and H), confirming expectations from theory. The SPDB Dataset. In a second demonstration, we used text from product failure complaints to examine the relationship between passive voice and psychological distancing. If a product failure experience is severe, the consumer may distance themselves from the experience by using passive voice when describing the event. We specifically used the "Safer Products" database, which includes consumer reports on product safety incidents. Consistent with our expectation, those narrating a "high severity" incident tended to use more passive voice than those writing about a "low severity" incident (statistical details in Web Appendix A3).
e. , the product) rather than the subject (i. e. , reviewer). On the other hand, use of passive voice can decrease perceptions of credibility and agency  (Kasof & Lee, 1993). Therefore, the use of passive voice might decrease an online review's helpfulness. The Yelp reviews dataset. We measured passive voice in online reviews published by Yelp. Given the volume of the dataset, we randomly selected 10% of the data resulting in a sample size of 699,028 online reviews. We examined the correlation between use of passive voice and perceived helpfulness of the reviews, finding that more passive voice was associated with more "useful" votes for that review (statistical details in Web Appendix A4). Finally, consumer psychologists could link rates of passive voice to charitable giving and fundraising, given that part of giving decisions are driven by how much donors perceive the creator of the campaign to be close to the cause and how much they believe in it.
Therefore, the use of passive voice, which is associated with distancing one from the message, might nudge the readers to infer a greater degree of distance between the creator of the campaign and the cause. The DonorsChoose Dataset. We used the DonorsChoose dataset to investigate the link between passive voice and fundraising success. We found a negative association between passive voice in campaign narratives and the campaign receiving full funding (statistical details in Web Appendix A5). Within the fully-funded campaigns, we also considered the time it took for campaigns to receive full funding and observed a similar pattern (statistical details in Web Appendix A5). Taken together, the use of passive voice relates negatively to fundraising success.
In doing so, we also provided initial empirical evidence to suggest how passive voice links to various consequential consumer outcomes. While passive voice is an important psychological indicator across different settings and research paradigms, its measurement is often indirect (e. g. , researchers use other language dimensions to proxy passive voice, such as auxiliary verbs) or unclear (e. g. , a sentence is deemed passive or active, but the decision-making process to determine passive or active voice is not transparent). In the next sections, we discuss the measurement side of passive voice by reviewing existing tools and describing our approach with PassivePy.
The second approach to measure passive voice uses proprietary software to determine grammatical voice. Ownby (2005) , for example, used WordPerfect to evaluate the linguistic and grammatical structure of mental health websites. The results suggested that readable and less readable sites did not differ in ratings of passive voice. Crucially, it is unclear how passive voice was determined or classified in this work, given that WordPerfect's analytic procedures are proprietary. Other studies (e. g. ,  Jia & Ying, 2015)  have used automated text analysis tools such as Coh-Metrix  (Graesser et al. , 2011; McNamara et al. , 2014)  to create an incidence score of "agentless passive voice" (e. g. , agentless passive voice phrases do not contain the word "by", such as "The professor was tricked"). While Coh-Metrix is a well-validated tool for social and psychological assessments of language, its passive voice classification system is also opaque.
Against this backdrop, our aim with this work is to create a reliable (e. g. , passive voice judgments are consistent), valid (e. g. , passive voice judgments are accurate), flexible (e. g. , texts can be assessed at different units of analysis), replicable (e. g. , the approach can be performed by a range of research teams with varying levels of computational expertise), and scalable way (e. g. , small and large collections of texts can be analyzed) to capture passive voice from different corpora for social and psychological evaluations of text. To achieve these aims, we introduce PassivePy, a fully transparent and documented Python library that can be applied to any number and size of texts in the social sciences. As our validation studies describe (see below), human coding and PassivePy are well-calibrated.
Labeling Passive Voice: A Seamless Approach with PassivePy In this section, we describe our approach with PassivePy, its internal mechanisms, and validate the tool across two studies. This process ensures that PassivePy is operating efficiently and effectively, while being user-friendly, for those who might have social science training instead of computer science training. To identify passive voice, PassivePy uses three analytic procedures concurrently: (1) Part-Of-Speech (POS) tagging using the open-source software library for advanced natural language processing: spaCy  (Honnibal & Montai, 2017) , (2) Dependency parsing using one of spaCy's pre-trained language models, and (3) Rule-based matching using one of spaCy's pre-trained models along with its vocabulary and language model. Specifically, we created a set of rules that could recognize various forms and structures of a sentence in passive voice.
The Benefits of PassivePy Before demonstrating PassivePy's reliability, validity, flexibility, replicability, and scalability, it is important to discuss its technical benefits as well. First, PassivePy is fast and efficient. To achieve fast performance and speed, our package uses multiprocessing pipelines, batched inputs, and pretrained models. Multiprocessing enables the package to process records using two or more central processing units within a single computer system. Batched records can be sent to various units of processing in parallel. Pretrained models avoid the need to train a large model before analysis. This helps the user to obtain their desired form of output quickly. For instance, PassivePy can process a dataset of 1150 one-sentence cases in less than 2 seconds. Moreover, our approach can process input texts on two different levels, the sentence level and the document level.
These are useful measures to identify passive voice prevalence in document-level texts, which can then be associated with other psychological and behavioral measures of interest. Crucially, at both the sentence-level and document-level analyses, PassivePy's output specifies the specific phrases (e. g. , in a separate column of the data) that were used in its determination of passive voice. This transparency is unavailable in most text analysis packages and therefore, the rules or decisions of PassivePy are clearly articulated. Lastly, the open-source nature of our package enables future researchers interested in examining aspects related to language structure and syntax (e. g. , grammatical subject, Ostinelli develop new tools. Having documented PassivePy's internal mechanics and benefits, in the next section, we validate PassivePy's performance on two different human-annotated datasets (https://osf.
Sample We used the Corpus of Linguistic Acceptability (CoLA) dataset from prior work  (Warstadt & Bowman, 2019) , which contained 1,043 manually-coded sentences from theoretical linguistics publications  (Warstadt et al. , 2018)  and labeled the sentences as containing a major linguistic feature (e. g. , a higher-order word classification such as a noun) or a minor linguistic feature (e. g. , a subunit of a major feature, such as complex noun phrase). Following sentence collection, the authors of the original paper labeled each sentence with binary codes indicating the presence (1) or absence (0) of such grammatical features (e. g. , syntax, verb tenses). The coding task was conducted by one of the authors trained in formal linguistics and linguistic annotation  (Warstadt & Bowman, 2019). We used the 1,043 sentences in English coded as either 0 (active) or 1 (passive).