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Inequality is an essential concept for understanding the impact of digital media on political life. This Element offers an empirical portrait of digital political inequality globally. We find that gaps in online political information reception and engagement are prevalent worldwide and growing over time. These inequalities are related to the resources held by individuals, the experiences of groups, and the economic, democratic, and technological development of societies. Moreover, we find that digital political media use is associated with greater participation in electoral politics and belief in democracy, while at the same time lower political trust and satisfaction with political systems. Based on these findings, we offer an agenda for studying digital political inequality across societal, technological, institutional, and individual levels. Ultimately, digital media not only create walls that separate the political haves and have-nots, but also windows and doors to greater political voice and influence for the less powerful.
Days alive out-of-hospital (DAOOH) is a well-accepted outcome in clinical research, as it summarizes both hospitalization and mortality. However, its distribution is often skewed, making accurate modeling challenging in practice. We utilized data from a randomized trial to discuss estimands and statistical methods of interest for DAOOH.
Methods:
We examined several estimands and statistical models targeting these estimands, including linear regression, gamma regression, ordinal regression, and the probabilistic index model. Comparison was based on interpretability, precision, and model fit. We adopted G-computation to assess causal effects leveraging different regression models to target a simple unified estimand.
Results:
For unadjusted analyses, the Mann–Whitney U-test is a simple and assumption-light approach, which can be paired with a probabilistic index to enhance interpretation. For adjusted analyses, while the mean difference and mean ratio are intuitive, the methods targeting them (linear and gamma regression) showed sub-optimal model fit. Conversely, ordinal regression showed a superior fit, but its odds ratio estimand is difficult to interpret. Pairing gamma or ordinal regression with g-computation facilitates estimation of a marginal causal mean difference estimand under certain assumptions, allowing for both a good model fit and interpretability.
Conclusions:
Our findings illustrate trade-offs of estimands and model choices for DAOOH, providing guidance for method selection. Based on data from this randomized trial, the Mann–Whitney U-test is recommended for unadjusted analyses. Ordinal regression paired with g-computation provides the best balance of precision, model fit, and interpretability to report an adjusted effect.
In Australia, ultra-processed foods (UPFs) contribute to 42% of total energy intake(1) and have been associated with excessive intakes of nutrients of concern for non-communicable diseases and increased odds of obesity(1,2). Evidence from systematic reviews have shown associations between UPF consumption and the risk of type-2 diabetes(3). Such prospective evidence is lacking in Australia. This study aimed to prospectively investigate the association between UPF consumption and risk of type-2 diabetes in Australian adults. Participants from the Melbourne Collaborative Cohort Study (MCCS) aged 40–69 years, without diabetes at baseline (1990–1994), and who provided dietary and follow-up data were included. Dietary assessment was conducted at baseline using a 121-item food frequency questionnaire developed for the MCCS, and UPFs were categorised using the Nova system. Incidence of type-2 diabetes was assessed at follow-up 1 (1995–2002; self-report) and follow-up 2 (2003–2007; self-report, elevated plasma glucose (≥ 7.0 mmol/l fasting or ≥ 11.1 mmol/l non-fasting), or medication use). Poisson regression models were used to estimate Incidence Rate Ratios (IRRs) and 95% confidence intervals (CIs) for the risk of type-2 diabetes per 10% increase and by quartiles of UPF consumption (as a percentage of total energy intake per day), using age as the time metric. Models were adjusted for sociodemographic (i.e., sex, age, marital status, country of birth, number of people occupying the household, education and Socio-Economic Indexes for Areas) and behavioural (i.e., smoking, physical activity and alcohol intake) variables. A total of 25,214 participants were included in the analysis (mean ± SD age = 54.3 ± 8.4 years at baseline, 61% female). In average, UPFs contributed to 40.4% of total energy intake, ranging from 25.6% in the first quartile to 55.8% in the fourth quartile. The proportion of males, individuals living in households with ≤ 2 people, those born in Australia, with lower education levels, greater socioeconomic disadvantage, and physically inactive increased with higher UPF consumption. During the follow-up period, 1,734 cases of type-2 diabetes occurred. Each 10% increase in UPF consumption was associated with an estimated 5% increase in risk of type-2 diabetes (IRR 1.05; 95% CI: 1.004–1.09; p-value = 0.031). Participants in the highest quartile of UPF consumption had a 14% higher risk of type-2 diabetes (IRRhigh (quartile 4) vs. low (quartile 1) category = 1.16, 95% CI: 1.01–1.34, p-value = 0.037). In this large cohort of Australian adults, higher UPF consumption was associated with an increased risk of type-2 diabetes. Limiting UPF consumption may reduce type-2 diabetes risk. These findings contribute to the body of research on the impacts of UPFs on chronic diseases, supporting recommendations to limit UPF consumption as part of a healthy diet.
[The Proto-Tocharian imperfect, based on IE optative formations, is preserved in Kuchean (Toch. B) but lost in Turfanian (Toch. A) except for two relics, the imperfects of the verbs ‘be’ and ‘go’. The remaining Turfanian imperfects are in origin identical with preterit formations found in Turfanian or in Kuchean or in both, which are all derived from IE perfects and aorists.]
The forty odd years which have elapsed since the first publication of a Tocharian text leave much for the comparativist to do. As clear as is the affinity of both dialects A and B to Indo-European, even so inexplicable are still many of the countless special developments that have led to their remarkable appearance alongside the other languages of the family. It is, in fact, the problem of the relationship of Tocharian to the other Indo-European dialects which has most interested scholars from the very beginning, while very little time has been devoted to the comparative study of the two dialects themselves. Indeed, their interrelationship is as yet quite imperfectly understood. As remarked already by M. Sylvain Levi, in speaking of the vocabulary, the more one observes their fundamental identity, the more one is struck by their divergences. The remark may apply no less appropriately to the phonology.
[This is intended as the first of a series of detailed discussions of the so-called Tocharian palatalization. All that is attempted here is an examination of the actual appearance of the sound change, to determine what are the original and what are the secondary consonants.]
For more than a generation now the bulk of scientific linguistic publication in America has been devoted to descriptive linguistics and the vast majority of the last generation of students of linguistics have devoted themselves largely to that field. The larger measure of scientific advance has accompanied the larger measure of devotion. That is to be expected. There have, of course, been notable exceptions. But I believe the facts will bear me out in my observation that these notable exceptions have been due to the continued labors of an older generation of scholars.
A generation or two ago the writers of handbooks of Indo-European comparative grammar were for the most part considerably more definite in their conclusions regarding the form of the parent speech than their successors are today. This is particularly true with regard to the verbal system. In those days the repertoire of verb categories was based largely on the agreements between Greek and Sanskrit, some of which were indeed striking, and the deviations of the other Indo-European languages were explained as developing out of that skeletal system, or as innovations, or were left without explanation.
[1. MWelsh bel, Welsh rhy-fel ‘war’ are not borrowed from Lat. bellum, but are derived from IE *gwel- ‘prick’. 2. Welsh blew, Bret, bleo ‘hair’ are derived from *mleus, extension of IE *mel- in Grk. μαλλός, etc. 3. Ir. brat ‘mantle’, Welsh brethyn ‘cloth’, etc. are derivatives of IE *bher- in Grk. . 4. Welsh cyw ‘chicken, young of animals’ < *kuw- : Grk. , etc. 5. Welsh etifedd ‘heir’ belongs to tyfu ‘grow’. 6. Welsh llafrog, Bret, lavrek ‘breeches’ are from IE *(s)lb- ‘slack, loose’. 7. Ir. re(i)the ‘ram’ is a derivative of the root of Ir. rethim ‘run’. 8. MIr. sáilim ‘expect’, NIr. saoilim ‘think’ are cognate with ON seilask ‘seek for’, ChSl. sila ‘strength’, etc. 9. Welsh traul ‘wear, expense’ is derived from IE *terā-: *ter- ‘rub‘.]