Since the mod extends the timeline and therefore game history, it also adds new bookmarks to help find out the main events in history. Some of the new ones are taken from other Paradox games and their mods.
Victoria 2 Extended Timeline
1856Summer: The steamer Propontis successfully lays cable across the Cabot Strait and the Newfoundland line is completed; now the telegraph has been extended one-third of the distance between New York and Europe. Field returns to England to raise more money.
N2 - Writers typically spend a certain proportion of time looking back over the text that they have written. This is likely to serve a number of different functions, which are currently poorly understood. In this article, we present two systems, ScriptLog+ TimeLine and EyeWrite, that adopt different and complementary approaches to exploring this activity by collecting and analyzing combined eye movement and keystroke data from writers composing extended texts. ScriptLog+ TimeLine is a system that is based on an existing keystroke-logging program and uses heuristic, pattern-matching methods to identify reading episodes within eye movement data. EyeWrite is an integrated editor and analysis system that permits identification of the words that the writer fixates and their location within the developing text. We demonstrate how the methods instantiated within these systems can be used to make sense of the large amount of data generated by eyetracking and keystroke logging in order to inform understanding of the cognitive processes that underlie written text production.
AB - Writers typically spend a certain proportion of time looking back over the text that they have written. This is likely to serve a number of different functions, which are currently poorly understood. In this article, we present two systems, ScriptLog+ TimeLine and EyeWrite, that adopt different and complementary approaches to exploring this activity by collecting and analyzing combined eye movement and keystroke data from writers composing extended texts. ScriptLog+ TimeLine is a system that is based on an existing keystroke-logging program and uses heuristic, pattern-matching methods to identify reading episodes within eye movement data. EyeWrite is an integrated editor and analysis system that permits identification of the words that the writer fixates and their location within the developing text. We demonstrate how the methods instantiated within these systems can be used to make sense of the large amount of data generated by eyetracking and keystroke logging in order to inform understanding of the cognitive processes that underlie written text production.
If successful, we would like to show that these can be feasibly implemented in future RCTs with additional benefit of reaching targeted sample sizes within the planned recruitment timeline due to increased recruitment rates.
There are a few suggestions/areas for consideration by the authors: The manuscript would benefit from the addition of a description of the role of a TPI should be included (as stated in section 1.3.2 of the extended protocol)
Reducing cervical screening frequency has been found to reduce overdiagnosis and overtreatment (Pienaar et al., 2019) and is associated with lower costs (Kim et al., 2017). However, participants of cancer screening programmes often react negatively to changes to cancer screening policy intervals and have limited awareness, or distrust of extended screening intervals (Ogden et al., 2020; Silver et al., 2015). For example, Gerend et al. (2017) found about 20% of women would not agree to less frequent screening even if recommended by their provider. International research suggests that women's unfavourable attitudes to increasing cervical screening intervals are due to perceived potential negative consequences, such as delayed diagnosis, increased risk of cervical cancer and death, and concerns about financial motives (Dodd, Nickel, et al., 2019; Dodd, Obermair, & McCaffery, 2019; Gerend et al., 2017; Hawkins et al., 2013; Obermair et al., 2018, 2020; Ogilvie et al., 2016). Furthermore, international studies have found that lower levels of knowledge and understanding of cervical screening is associated with lower levels of willingness to extend screening intervals (Cooper et al., 2015; Hawkins et al., 2013).
Qualitative content analysis found that perceived benefits of extending the interval between screens included convenience of having fewer tests and appointments, reduced physical discomfort and reduced psychological distress due to fewer cervical screens. Perceived harm of fewer cervical screens included increased risk of developing precancerous cell changes, increased psychological distress due to being unaware of what is going on in the body, and increased risk of developing cervical cancer. Around a fifth of participants explicitly reported that they could see no possible benefit to extending screening frequency, while less than 1% reported perceiving no possible risks. The most frequently cited concern was a perception that a longer interval between screenings may mean that there would be more time for disease to develop undetected. Taken in combination with the finding that women who attend regularly and intend to attend when invited are more likely to object to an increased interval between screening, this may suggest that women's attitudes to screening frequency are informed by a perception that more frequent screening leads to improved disease prevention. The second most frequently cited perceived harm was increased risk of cancer. In combination with the multivariate analysis finding that women who considered themselves to be at higher risk from cervical cancer were more likely to be against a decrease in screening frequency, this qualitative finding may suggest that a belief that more frequent cervical screening decreases risk of cervical cancer is a factor informing women's attitudes to screening frequency. Both of these findings support Hill et al.'s (2021) conclusion that understanding the HPV timeline (length of time between HPV infection and cervical cancer development) might increase acceptability of reduced screening frequency.
The Abortion Act 1967 made abortions by regulated practitioners operating in the National Health Service legal. The law extended to all parts of the UK except Northern Ireland, where it became legal in 2020. In addition, 1967 saw the Sexual Offences Act 1967, which decriminalized private homosexual acts between two men over the age of 21. Both of these laws represented a greater liberalization of social and moral views.
This year was dominated by the referendum on the UK's membership in the EU, which took place on 23 June 2016. On a turnout of 72.2 per cent, 51.9 per cent (17,410,742 votes) voted to leave the EU, and 48.1 per cent (16,141,241 votes) voted to remain. Known as Brexit, this decision has dominated the political landscape ever since. Following the vote, David Cameron stepped down as Conservative Party leader and prime minister and was replaced in July by Theresa May. Nigel Farage resigned as UKIP leader. Prior to the Brexit vote, Sadiq Khan replaced Boris Johnson as the mayor of London, becoming the first Muslim mayor of London. The collapse of the NHS was forecast with increases in waiting times, and there was a rise in pollution caused by commuters and vehicles. New forests were proposed to restore the countryside, and the Lake District and Yorkshire Dales national parks were extended by 188 square miles. In the world of sport, Leicester City won the Premier League for the first time, having started the season as 5,000:1 outsiders for the title.
Only relatively recently have attempts been made to describe events systematically. Yet events are integral to software engineering which uses formalisms such as state-machines, Petri Nets, state charts, transition networks, and programming languages. Events have also played a growing role in formal approaches to narrative (e.g., [7, 16]), in document preservation metadata (e.g., [15]), in linguistics (e.g., [9]), and in cognitive science (e.g., [24]). Some formal models for events are beginning to be applied to the study of history; these include text processing of historical newspapers [4], models for interactive timelines [2], analysis of named events [21, 22], and specification of events using semantic web notation [18, 23].
While traditional timelines present separate events without context or inter-connection, we have developed timelines which tell a story or provide an explanation for how the events are related. Some of this work was based on models of plots in narratives (e.g., [2, 16]). [2] developed an interactive narrative timeline to present an overview of several explanations for the outbreak of the American Civil War. The interface supports the presentation and browsing of several different causal threads (Figure 1).
We propose that systems of information organization for rich content such as historical texts be based on a "fabric" of entities and events. In developing the entity-event fabric, we explore the use of verb frames from the FrameNet project. Thus, we envision an incremental development in which the fabric initially is used for relatively simple applications (e.g., Figure 1) and its scope is gradually extended. As described in [6], this fabric would also be extended with discourse elements. Eventually, the schematic environments could be used for interactive text generation such as question answering systems, tutoring systems, and conversational historical agents (see [6]). This work in indexing historical texts stems from previous work on text mining historical newspapers and on designing new types of timelines (see Figure 1). This work also complements our research on conceptual modeling of scientific research reports [3, 5] even though historical texts are much less structured than scientific research reports.
Our approach may usefully be deployed on a small scale in ad hoc situations. This approach can be extended to cover large collections of rich content although the sheer scale of that task makes it challenging. One advantage compared to some previous projects is that we are primarily focused on description rather than inference. With inference, small errors can be easily amplified. 2ff7e9595c
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