Психолінгвістичні аспекти маніпулятивного перекладу у медійному просторі
Bibliographic record
Abstract
This article covers the issue of manipulative translation as one of the techniques to control the consciousness of the audience. The author indicates that such a type of translation is a supplement tool for influencing the people who are consumers of common media space characterized by the common language, national and territorial belonging. In the article, the text translation concerning the current political issues and news which took place in the world and in Ukraine, are analyzed. Among the main sources were national and international news reports. Using scientific analysis the original source and its translation variants were compared. As a result, the author drew a conclusion that for the manipulative translation the same techniques are used, five of them being viewed as techniques of manipulative translation. The examples of their implementation show how the manipulative translation misrepresents the source information. Our understanding is that the manipulative translation in combination with other manipulation techniques works as savvy propaganda. References Крилова-Грек Ю. М. Психолінгвістичні особливості перекладу семантичних одиниць:Дис.... канд. психол. наук: 19.00.01. Київ, 2007.Krylova-Grek, Y.M. (2007). Psycholingustychni osoblyvosti pereklady semantychnyh odynyts[The psycholinguistic peculiarities of semantic units]. Ph.D. dissertation. Kyiv. Кара-Мурза С. Г. (2007), Манипуляции сознанием. М.: Эксмо, 2007.Kara-Murza, S. G. (2007). Manipuliaciya Soznaniem [Consciousness Manipulation]. Moscow:Eksmo. Шмелев И. В. Историческая ретроспектива договора Вайтанги // Вопросы современнойюриспруденции: Сб. ст. по матер. V междунар. науч.-практ. конф. № 5. Новосибирск:СибАК, 2011. Режим доступа: https://sibac.info/conf/law/v/37958.Shmelov, I.V. (2011). Istoricheskaya retrospectiva dogovora Waitangi [The historicalretrospective of Waitangi treaty]. Voprosy Sovremennoy Yurysprudentsyy. ConferenceProceedings, 5. Retrieved from: https://sibac.info/conf/law/v/37958. Tremblay, G. (2014). From Marshall McLuhan to Harold Innis, or From the global village tothe world empire. Canadian Journal of Communication, 37(4). Sources Вице-президент США назвал Украину самой коррумпированной страной в мире.(2015, Апрель 11). Expert Online Vice-prezident ssha nazval ukrainu samoikorrumpirovannoi stranoi v mire. (2015, April 11). Retrieved from:http://expert.ru/2015/12/8/vitse-prezident-ssha-nazval-ukrainu-samoj-korrumpirovannojstranoj-v-mire/ Forbes: Украине пора перестать строить из себя принцессу, ожидающую рыцаря.(2016, Март 27). Новостное агентство Харьков. Forbes: Ukraine pora perestat stroit iz sebya princessu, ozhidayushhuyu rycarya. (2016,Mart 27). Novostnoe agentstvo Kharkov. Retrieved from: https://nahnews.org/657737-forbes-ukraine-pora-perestat-stroit-princessu-v-ozhidanii-rycarya-na-belom-kone. Николай Лазаренко (2015, Червень 2). FT: назначение Саакашвили – начало большихпроблем Порошенко.Nikolai Lazarenko (2015, June 2). FT: naznachenie saakashvili – nachalo bolshix problemporoshenko. Retrieved from: https://ria.ru/world/20150602/1067801366.html Трамп назвав Путіна “твердим печивом”. (2017, Березень 19). ЦензорНет.Trump nazvav Putina “tverdym pechyvom”. (2017, Berezen’ 19). TsenzorNet Retrievedfrom: http://ua.censor.net.ua/news/43 2556/ tramp_nazvav_putina_tverdym_pechyvom. Мария Бондаренко (2017, Март 19). Трамп назвал Путина «крепким орешком».Mariya Bondarenko (2017, Mart 19). Trump nazval putina «krepkim oreshkom».Retrieved from: http://www.rbc.ru/society/19/03/2017/58cde8f49a794717c2202ef4 “Уточнение перевода”: Кремль изменил цитату из письма Эрдогана к Путину. (2016,Червень 28). Інформаційна агенція УНІАН. “Utochnenie perevoda”: Kreml izmenil citatu iz pisma erdogana k putinu. (2016, Cherven’28). Informatsiyna ahentsiya UNIAN. Retrieved from:https://www.unian.net/world/1390007-kreml-izmenil-tsitatu-iz-pisma-erdogana-kputinu.html. Doug Bandow (2016, March 25) Busted Fantasies In Kiev: America And Europe Won’tSave Ukrainian Maiden In Distress. Forbes. Retrieved from:https://www.forbes.com/sites/dougbandow/2016/03/25/busted-fantasies-in-kiev-americaand-europe-wont-save-the-ukrainian-maiden-in-distress/3/#4243b00e6166 From Berezovsky to Erdogan: who and what were apologizing for Putin. (2017, February7). FreeNews English. Retrieved from: http://freenews-en.tk/2017/02/07/fromberezovsky-to-erdogan-who-and-what-were-apologizing-for-putin/ Jon Snow [The Viral Network]. (2017, March 19). Trump: “Putin Is One Tough Cookie”(19.03.2017). Retrieved from: https://www.youtube.com/ watch?v=glVY2afl_d4 Paul Meredith, Rawinia Higgins Kāwanatanga. Māori engagement with the state / PaulMeredith, Rawinia Higgins Kāwanatanga. Retrieved from:http://www.teara.govt.nz/en/kawanatanga-maori-engagement-with-the-state/page-1#2 Remarks by Vice President Joe Biden to The Ukrainian Rada. (2015, December 9). TheWhite House, Office of the Vice President. Retrieved from:https://obamawhitehouse.archives.gov/the-press-office/2015/12/09/remarks-vicepresident-joe-biden-ukrainian-rada. Tony Barber (2015, June 2). Odessa appointment raises questions over Poroshenko’sjudgement. Financial Times. Retrieved from: https://www.ft.com/content/1751c125-d923-3755-88e7-c5378a952578
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".