@article{RomanelloWalawenderHsuetal., author = {Romanello, Marina and Walawender, Maria and Hsu, Shih-Che and Moskeland, Annalyse and Palmeiro-Silva, Yasna and Scamman, Daniel and Smallcombe, James W and Abdullah, Sabah and Ades, Melanie and Al-Maruf, Abdullah and Ameli, Nadia and Angelova, Denitsa and Ayeb-Karlsson, Sonja and Ballester, Joan and Basaga{\~n}a, Xavier and Bechara, Hannah and Beggs, Paul J and Cai, Wenjia and Campbell-Lendrum, Diarmid and Charnley, Gina E C and Courtenay, Orin and Cross, Troy J and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Eckelman, Matthew and Freyberg, Chris and Garcia Corral, Paulina and Gasparyan, Olga and Giguere, Joseph and Gordon-Strachan, Georgiana and Gumy, Sophie and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Heidecke, Julian and Hess, Jeremy J and Jankin, Slava and Jay, Ollie and Pantera, Dafni Kalatzi and Kelman, Ilan and Kennard, Harry and Kiesewetter, Gregor and Kinney, Patrick and Kniveton, Dominic and Koubi, Vally and Kouznetsov, Rostislav and Lampard, Pete and Lee, Jason K W and Lemke, Bruno and Li, Bo and Linke, Andrew and Liu, Yang and Liu, Zhao and Lowe, Rachel and Ma, Siqi and Mabhaudhi, Tafadzwanashe and Maia, Carla and Markandya, Anil and Martin, Greta and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan and Mohajeri, Nahid and Momen, Natalie C and Moradi-Lakeh, Maziar and Morrisey, Karyn and Munzert, Simon and Murray, Kris A and Obradovich, Nick and Orgen, Papa and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia L and Pega, Frank and Pershing, Andrew J and Pinho-Gomes, Ana-Catarina and Ponmattam, Jamie and Rabbaniha, Mahnaz and Repke, Tim and Roa, Jorge and Robinson, Elizabeth and Rockl{\"o}v, Joacim and Rojas-Rueda, David and Ruiz-Cabrejos, Jorge and Rusticucci, Matilde and Salas, Renee N and San Jos{\´e} Plana, Adri{\`a} and Semenza, Jan C and Sherman, Jodi D and Shumake-Guillemot, Joy and Singh, Pratik and Sj{\"o}din, Henrik and Smith, Matthew R and Sofiev, Mikhail and Sorensen, Cecilia and Springmann, Marco and Stowell, Jennifer D and Tabatabaei, Meisam and Tartarini, Federico and Taylor, Jonathon and Tonne, Cathryn and Treskova, Marina and Trinanes, Joaquin A and Uppstu, Andreas and Valdes-Ortega, Nicolas and Wagner, Fabian and Watts, Nick and Whitcombe, Hannah and Wood, Richard and Yang, Pu and Zhang, Ying and Zhang, Shaohui and Zhang, Chi and Zhang, Shihui and Zhu, Qiao and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline}, series = {The Lancet}, volume = {406}, journal = {The Lancet}, number = {10521}, publisher = {Elsevier BV}, doi = {10.1016/S0140-6736(25)01919-1}, pages = {2804 -- 2857}, language = {en} } @article{BecharaHerzogJankin, author = {B{\´e}chara, Hannah and Herzog, Alexander and Jankin, Slava}, title = {Transfer learning for topic labeling: Analysis of the UK House of Commons speeches 1935-2014}, series = {Research and Politics}, volume = {April-June 2021}, journal = {Research and Politics}, doi = {10.1177/20531680211022206}, abstract = {Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.}, language = {en} } @article{WeeksJankinMikhaylovHerzogetal., author = {Weeks, Liam and Jankin Mikhaylov, Slava and Herzog, Alex and {\´O} Fathartaigh, M{\´i}che{\´a}l and Bechara, Hannah}, title = {It's Only Words? Analysing the Roots of the Irish Party System Using Historical Parliamentary Debates}, series = {Parliamentary Affairs}, journal = {Parliamentary Affairs}, issn = {0031-2290}, doi = {10.1093/pa/gsac020}, abstract = {While the public image of legislative debates is often less than favourable, parliamentary deliberations can be an important indicator of policy preferences, issue saliency and cohesion within political parties. We consider the case of a parliamentary debate that had a considerable long-term political legacy, forging a party system that endured for almost a century. The debates in the Irish parliament over the 1921 Anglo-Irish Treaty were a critical juncture that split a dominant party, resulting in, first, a civil war and, later, a new mode of party competition. We analyse the text of the debates from this period to see if they contribute to a greater understanding of the ensuing split. Few differences between the two sides in parliament are found, which might explain why few were the differences between the key actors in the party system that evolved.}, language = {en} } @article{vanDaalenRomanelloRockloevetal., author = {van Daalen, Kim R and Romanello, Marina and Rockl{\"o}v, Joacim and Semenza, Jan C and Tonne, Cathryn and Markandya, Anil and Dasandi, Niheer and Jankin, Slava and Achebak, Hicham and Ballester, Joan and Bechara, Hannah and Callaghan, Max W and Chambers, Jonathan and Dasgupta, Shouro and Drummond, Paul and Farooq, Zia and Gasparyan, Olga and Gonzalez-Reviriego, Nube and Hamilton, Ian and H{\"a}nninen, Risto and Kazmierczak, Aleksandra and Kendrovski, Vladimir and Kennard, Harry and Kiesewetter, Gregor and Lloyd, Simon J and Lotto Batista, Martin and Martinez-Urtaza, Jaime and Mil{\`a}, Carles and Minx, Jan C and Nieuwenhuijsen, Mark and Palamarchuk, Julia and Quijal-Zamorano, Marcos and Robinson, Elizabeth J Z and Scamman, Daniel and Schmoll, Oliver and Sewe, Maquins Odhiambo and Sj{\"o}din, Henrik and Sofiev, Mikhail and Solaraju-Murali, Balakrishnan and Springmann, Marco and Tri{\~n}anes, Joaquin and Anto, Josep M and Nilsson, Maria and Lowe, Rachel}, title = {The 2022 Europe report of the Lancet Countdown on health and climate change: towards a climate resilient future}, series = {The Lancet Public Health}, volume = {7}, journal = {The Lancet Public Health}, number = {11}, publisher = {Elsevier BV}, issn = {2468-2667}, doi = {10.1016/s2468-2667(22)00197-9}, pages = {E942 -- E965}, language = {en} } @article{BecharaOrăsanParraEscartinetal., author = {B{\´e}chara, Hannah and Orăsan, Constantin and Parra Escart{\´i}n, Carla and Zampieri, Marcos and Lowe, William}, title = {The Role of Machine Translation Quality Estimation in the Post-Editing Workflow}, series = {Informatics}, volume = {8}, journal = {Informatics}, number = {3}, doi = {10.3390/informatics8030061}, abstract = {As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators' cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators' cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.}, language = {en} }