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                                     Lexical Cohesion

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Lexical cohesion involves the use of items of vocabulary to create connections between different parts of a text.  This is usually referred to as cohesion.  This in turn creates coherence which makes the message in a text easy to follow.  In order to avoid repetition of words, a writer can use;

  • synonyms - words of similar meaning,

  • antonyms - words of opposite meaning,

  • hyponyms - words which are used to group items together, and

  • lexical sets which are words used to describe different aspects of a topic. 

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However, the most common way of creating cohesion in academic text is the use of lexical sets.  These are words that are related due to the context in which they are used.  If we look at the abstract explaining the research carried out on criminal profiling, we can identify a number of content words that are related and in turn create cohesion within the text.

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Example 1

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The scholarly literature over the past decade has chronicled a growing problem in the forensic technique colloquially called criminal profiling. The basis of this conundrum appears to originate from a concept referred to as “offender homology,” which presumes an inherent uniformity among offenders that is believed to underpin the analytic process incumbent to criminal profiling. Studies thus far conducted have apparently struggled to find evidence of offender homology and based upon these findings arguments have been promulgated that various approaches to criminal profiling imputably labeled as “trait-based” are therefore not viable. Indirectly contradicting these arguments, however, have been studies testing profiler accuracy that have found evidence of individuals who appear to use trait-based methods but can nonetheless proficiently predict the characteristics of unknown offenders. Against this backdrop, the present article examines a number of tenets and disjunctions that appear to have arisen from research into offender homology and imputed to the practices of so-called trait-based profiling. The notion of whether trait-based profiling is, in fact, representative of profiling methods is examined, and an integrative hypothesis proposed that attempts to resolve the quandary between offender homology and profiler accuracy.

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Kocsis, R.N. and Palermo, G.B., 2015. Disentangling criminal profiling: Accuracy, homology, and the myth of trait-based profiling. International journal of offender therapy and comparative criminology, 59(3), pp.313-332.  

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Notice the repetition of key lexical items.  This is because there are no possible synonyms or antonyms for these words and changing them would result in the text being either difficult to understand, or not academic in tone.  It is perfectly acceptable to use such terms in your own writing.  However, notice that ‘offender homology’ and ‘trait-based’ are between speech marks as they are possibly concepts developed by other researchers.  As a student of criminology, you would be expected to know, understand and use these key concepts.  

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*It is important to note that different academic disciplines have their own vocabulary and ways of expressing their findings and ideas. Therefore it is important that you make a note of any new key words  or phrases that you find in your reading.  

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There are many ways to organise your vocabulary.  For example, you could create your own dictionary using suitable software.  

If you do this, you should record any features of the item such as noun, verb, adjective, adverb, that will help you to use it in the future.

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  • the word form e.g. analysis (n)

  • related word forms e.g. analyse/analyze (v) analytical (adj)

  • meaning (remember to consider the context)

  • translation (if you feel it is necessary)

  • a sentence that helps you to understand the meaning

  • organise them into lexical sets, or groups with a specific heading

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Identifying the way a text is linked can help the reading process by creating a flow of ideas that are easy to follow.

 

Looking at the text on climate change we can see that lexical cohesion works in different ways.  

 

Example 2

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This example demonstrates how the topic under discussion, climate change, is repeated helping the reader to follow the discussion.

 

For generations, climate scientists have educated the public that ‘weather is not climate’, and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades. However, weather when considered globally is now in uncharted territory. Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming. Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data. Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

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Sippel, S., Meinshausen, N. and Fischer, E., Eniko SzA© kely, and Reto Knutti. Climate change now detectable from any single day of global weather. Nature Climate Change, 10, pp.35-41.

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Repetition of the words climate change and climate give the topic, while weather and global add context.  

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*Key concepts such as climate change do not have synonyms.

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If we look at the same text, we can see that the highlighted words add specific meaning and context to the overall discussion of climate change.

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For generations, climate scientists have educated the public that ‘weather is not climate’, and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades. However, weather when considered globally is now in uncharted territory. Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming. Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data. Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

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Sippel, S., Meinshausen, N. and Fischer, E., Eniko SzA© kely, and Reto Knutti. Climate change now detectable from any single day of global weather. Nature Climate Change, 10, pp.35-41.

 

Our final look at the same text highlights the specific language that is used when discussing research findings.  Remember to create your own dictionary with discipline specific language..  This language could be used in any academic paper as it is not specific to any discipline.  Also, such language would not be considered plagiarism as it has no specific meaning or relevance to this or any other research.  

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For generations, climate scientists have educated the public that ‘weather is not climate’, and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades. However, weather when considered globally is now in uncharted territory. Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming. Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data. Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

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Sippel, S., Meinshausen, N. and Fischer, E., Eniko SzA© kely, and Reto Knutti. Climate change now detectable from any single day of global weather. Nature Climate Change, 10, pp.35-41.

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See the section on Chunking for more information on how to identify useful language that can be used in your own writing.

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Practice

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Now it's time to focus your activity on texts that you may need to read and understand for your own discipline.  Nowadays academic papers from relevant, peer-reviewed, academic journals are the main resource for research for your assignments.  You can use your institution's database which will give you access to a wealth of resources, or if you are not enrolled in an institution, you can use Google Scholar which will give you abstracts for numerous texts on all academic disciplines.  

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Using an abstract or introduction to an academic paper in your discipline or area of interest, skim the text to understand the main idea. Now,

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  • read the text again and underline any words, collocations or expressions that specifically explain the main idea.

  • organise the words to help you to understand and remember them.

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There are many ways to organise your vocabulary.  For example, you could create your own dictionary using suitable software.  

If you do this, you should record any features of the item that will help you to use it in the future.

​

  • the word form e.g. analysis (n)

  • related word forms e.g. analyse/analyze (v) analytical (adj)

  • meaning (remember to consider the context)

  • translation (if you feel it is necessary)

  • a sentence that helps you to understand the meaning

  • organise them into lexical sets, or groups with a specific heading

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