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Open University E303 TMA03 : Comparative register analysis

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Introduction

TMA 03 E303 Oksana Markova Comparative register analysis Transcriptions of both texts are enclosed as Appendices and for ease of reference the line numbers given in brackets. The line numbers and bibliographic references are not included in the word count. Text 1 Text 2 Register analysis Linguistic evidence Register analysis Linguistic evidence FIELD (Experiental meta-function). Semantic domain Topic: Census Professionals and organisations Inconvenience Questions Costs Vocabulary Lexical choice examples: Remarkable resource, bygone legacy, the 2001 UK Census, national censuses. many academic and applied practitioners the invasion of their privacy question on income, an income question, information on their income, a question on etnicity, the question. National censuses are expensive, cost £259 million Formal Focus is on questioning whether census is worthy considering the cost and time and all the difficulties in calculations and inconvenience involved. Semantic domain Topic: Census Professionals and organisations Inconvenience Questions Costs Vocabulary Lexical choice examples: This, this questionnaire thingy, the census, the fella, this thing, it Big Brother, they, the government a step too far those ethnic questions, and this one, questions about work and stuff, how much you get paid Waste of money, cost a fortune to send this thing out Informal colloquial We can notice a little bit different focus in the text it is more about what personal inconvenience census cause to interactors. Specialization Specialist/expert terms Vocabulary used assumes a certain degree of knowledge. Article aimed at non-expert but rather educated reader who is familiar with lexical words used and has certain degree of knowledge of a census in broader view. Publication seems more like a broadsheet rather than tabloid. (aimed at less-educated reader) Specialization Specialist/expert terms Non-specialist conversation. Involves discussion of census, but no factual information Vocabulary refers to a different not as well-educated audience. Casual colloquial formulations/wordings like “dunno”, “the fella”, …“chatty” conversational vocabulary occurring in “real” conversation. Angle of representation Characterisation of events/entities Process types Participants Agent de-emphasis Author uses an interesting way to lead the readership to view the question of census in a certain way. ...read more.

Middle

lack of a question on income The addition of a question on ethnicity The concerns The Text 1 is rich on different types of describers and classifiers in noun phrases which is common for written modes. Ex. Amount/size describers short periods (8) and low wages (8), time describer recent estimates (3), evaluative describer basic criteria (6) and topical classifiers: national censuses (3, 13), academic and applied practitioners (11), geographical origin (24). There are no personal pronouns in the text which is natural for non-interactive nature registers. Nouns in general frequent and even nouns pre-modifying nouns: The 2001 UK census (1, 4), pencil and paper era (1), income question (15) High frequency of prepositions of -12, on-7, to -7, in -4, as-6. Also due to high number of nouns. A lot of nouns as part of a prepositional phrase (circumstance): invasion of their privacy (6), question on income (11), burden of the time (5) assessment of the implications (13), reliability of the returns (15) question on etnicity (19) information on etnicity (26) information on their income (16) the need for a census (4) the type of information (10) the lack of a question on income (11) the addition of a question on etnicity (19) Equally frequent in text describers (ex.: recent estimates (3), short periods (8), low wages (8)) and classifiers (ex.: national censuses (3), academic practitioners (11), geographical origin (24)). Some complex pre- and post-modification head nouns (ex.: the type of information that is collected (10), the uncosted burden of the time taken by the population to complete it (5). Nominalisation is quite frequent including a careful assessment of implications (13), the addition of a question (18), the need for a census (4). High frequency of determiners in the text. Occurrence numbers: the-26, a -7, that -5, such -5, other -3. Especially frequent the articles the and a, mostly due to large number of nouns. ...read more.

Conclusion

Er OK, it’s only 4 pages per person. A: Still 4 pages too many. B: And why did they have to make it this revolting purple colour? A: Because that’s the colour most people’s faces turn when they try to fill it in. B: If you’ve got nothing constructive to say then go and put the kettle on or something. A: OK but seriously how how are you going to answer all those ethnic questions? B: Er um I er I dunno. This wasn’t really designed for someone like me. A: What does it make er matter anyway? You’re British. Who cares where your grandparents were from. B: The government obviously does. A: Well at least they don’t ask you how much you get paid. (B: Hmm) That’d be a step too far. B: No, that’s true but there’s one and a half pages of other questions about work and stuff. A: Oh you’re joking. There’s no fff there’s no way I’m filling that thing in. B: Suit yourself. Appendix II List of frequencies for Text 1 List of frequencies for Text 2 26 8.1505% the 12 3.7618% of 9 2.8213% and 8 2.5078% question 7 2.1944% is 7 2.1944% on 7 2.1944% to 7 2.1944% a 6 1.8809% as 5 1.5674% was 5 1.5674% has 5 1.5674% that 5 1.5674% uk 5 1.5674% such 5 1.5674% census 4 1.2539% been 4 1.2539% for 4 1.2539% in 4 1.2539% it 4 1.2539% 2001 3 0.9404% income 3 0.9404% other 3 0.9404% information 3 0.9404% also 15 3.9063% you 13 3.3854% to 11 2.8646% õs 10 2.6042% it 10 2.6042% this 9 2.3438% and 8 2.0833% i 8 2.0833% a 7 1.8229% that 6 1.5625% the 6 1.5625% have 5 1.3021% how 5 1.3021% in 5 1.3021% er 5 1.3021% õt 5 1.3021% what 4 1.0417% they 4 1.0417% of 4 1.0417% no 4 1.0417% pages 4 1.0417% oh 4 1.0417% õre 4 1.0417% there 3 0.7813% got 3 0.7813% is 3 0.7813% not 3 0.7813% all 3 0.7813% me 3 0.7813% does 3 0.7813% on 3 0.7813% thing 3 0.7813% at 3 0. ...read more.

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