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Gender advert workbook
1.
Language, gender and
online shopping An investigation The scenario You are on work experience at the online shopping company which markets its gift products on www.giftinspiration.com. The Marketing Director has received a letter of complaint which argues that the ‘Gifts for Men’ and ‘Gifts for Women’ webpages are a morally contemptible misrepresentation of men and women, and that the product descriptions use language in an unacceptable sexist manner. The Marketing Director is very surprised as the Customer Testimonials have always been extremely positive: these can be viewed on the website. Wary of sensitivity surrounding matters of social justice, and keen to do the right thing as far as possible within a commercial context, s/he wants to know if the allegations have any substance. S/he asks you to investigate the complaint and to report back with your findings. Your report can be a written document or a verbal presentation. This complaint is a serious concern to the company, so the evidence must be well documented and your conclusions clear. Introduction Write a short introduction to your research project. The audience for this is the Marketing Director of www.giftinspiration.com. Start by making brief notes under these headings if you find it helpful to do so: • Some reasons why this investigation is important for the company. • Any ideas about the nature of online shopping that help to explain why getting the marketing language right can be a difficult task. • How the investigation might help the development of the website. Data description Give basic factual information about the data you are investigating. • You are investigating short product descriptions designed to sell gift items on an online gift shopping website, specifically items on webpages entitled ‘Gifts for Men’ and ‘Gifts for Women’. State how many product descriptions are in each data set. • Where and when was the data collected? (Give the URL and the date accessed.) • Was the data collected with the consent of its owner? © 2007 www.teachit.co.uk 8057 Page 1 of 22
2.
Language, gender and
online shopping An investigation Aims Explain that you are/were hoping hoped to find out the answer to this question: • Is the representation of men and women in the ‘gifts for men’ and ‘gifts for women’ webpages sexist? Then explain that this is broken down into two smaller questions, as follow. Write in some brief notes under each question, explaining what answers you expect/expected to find. We will compare the ‘real’ answers afterwards. This process is called forming a hypothesis and is an important part of a research project. You mustn’t worry that what you write here will be ‘wrong’. The whole point of research is to test ideas out to find the truth. • What stereotypes and assumptions about male and female behaviour and interests do the product descriptions appeal to? • How do the product descriptions use language in gendered ways? Methodology Explain how you did your research. The basic facts are given below, but you should add some detail to each of these as you work through the tasks. You should try to say something about the merits of these methods. Be aware of any limitations of these methods as you complete the tasks as this will be helpful for your evaluation. Question 1: What stereotypes and assumptions about male and female behaviour and interests do the webpages appeal to? 1) Identified how many of the product descriptions belonged to each product type to determine if there were any gendered patterns [TASK 1 below]. 2) Compared ‘male’ and ‘female’ items of the same product type to determine if there were any gendered patterns [TASK 2 below]. Question 2: How do the product descriptions use language in gendered ways? 1) Corpus investigation to generate Keyword Lists for both data sets [TASK 3 below]. 2) Investigation of keyword lists for both data sets to explore interesting patterns of gender differences and to test these against key markers of language and gender according to popular beliefs and research ideas. [TASK 4 below]. © 2007 www.teachit.co.uk 8057 Page 2 of 22
3.
Language, gender and
online shopping An investigation Findings and analysis This section should give a detailed and organised analysis of your discoveries. This means saying what you discovered as clearly as possible, but also explaining what you think it means, what is interesting about it, or why it matters. You could use charts, tables and screenshots to help make some of your points clear. This section should be divided into sub-sections with relevant headings to make it easier for your readers to follow your explanation. You should start by summarising your thoughts in response to the bullet points under each of the headings below. You could go on to use these as headings in your report, but feel free to change them in ways that better suit your findings. Gender representation in range and frequency of product types • What might it be reasonable for an online shopper to expect in terms of this aspect of gender representation? • What patterns of gender representation have you observed in the data under this heading? • What is interesting about your observations? Why are they important? What do they mean? In answering these questions think about the challenge of marketing products to online shoppers. Gender representation in products of the same type • What might it be reasonable for an online shopper to expect in terms of this aspect of gender representation? • What patterns of gender representation have you observed in the data under this heading? • What is interesting about your observations? Why are they important? What do they mean? In answering these questions think about the challenge of marketing products to online shoppers. Gender representation in semantic fields analysis • What might it be reasonable for an online shopper to expect in terms of this aspect of gender representation? • What patterns of gender representation have you observed in the data under this heading? • What is interesting about your observations? Why are they important? What do they mean? In answering these questions think about the challenge of marketing products to online shoppers. © 2007 www.teachit.co.uk 8057 Page 3 of 22
4.
Language, gender and
online shopping An investigation Gender representation in noun pre-modifiers analysis • What might it be reasonable for an online shopper to expect in terms of this aspect of gender representation? • What patterns of gender representation have you observed in the data under this heading? • What is interesting about your observations? Why are they important? What do they mean? In answering these questions think about the challenge of marketing products to online shoppers. Conclusions Explain to the Marketing Director the conclusions you are able to draw from your analysis. To do this, go back to the original question(s) and consider what answer(s) you can now give. You might like to consider how accurate your hypotheses were. Be open- minded: don’t force the data to fit what you hoped to see. If your conclusions weren’t what you expected, discuss why you think this may be the case. In giving your conclusions, it is appropriate to be tentative. Due tentativeness could come from: • mentioning any biases the methodology may have produced, for example that you were working with a very small data set, and without any comparison with other online gift marketing websites • touching on different ways the same findings could be interpreted by other people In an ‘applied’ language investigation of this kind, you should then present recommendations for action, based closely on your conclusions. You could put these under a new heading. In this case, summarise for the Marketing Director any points about website content and style that you would recommend any changes to, in order to improve the company’s public image in relation to gender representation. You could do this in bullet-point format if you like. (Busy directors will always read this page first, so make sure it’s good but very quick and easy for them to read!) You should include one product description from each data set rewritten in a more appropriate style. Alternatively, if you believe no changes are necessary to website content or style, summarise the key arguments you would recommend in order to respond to the letter of complaint. You could do this in bullet-point format if you like, or you could write a draft letter for the Marketing Director to edit. © 2007 www.teachit.co.uk 8057 Page 4 of 22
5.
Language, gender and
online shopping An investigation Evaluation In this section you review how good you think this piece of research was. Start by making some brief notes in response to each of these questions. How good was the data we used? (ie the product descriptions texts) Where there any limitations with the analytical method we used? What have you learned that you think might be useful for future research projects? © 2007 www.teachit.co.uk 8057 Page 5 of 22
6.
Language, gender and
online shopping An investigation TASK 1 Start by skim reading the two sets of gift description texts (‘Gifts for Men’ and ‘Gifts for Women’) to identify what sort of product each set contains. Use the table of product types below to help you and record your findings in the tables that follow. You could check your answers on the Gift Inspiration website (www.giftinspiration.com) but be warned of two things (1) their electronic classification allows for multiple tagging whereas this paper data record sheet encourages you to choose one product type and (2) the website is updated periodically and the data may not match what is in this resource. Product types (adapted from the classification used on www.giftinspiration.com) Extra rows are provided in case you want to need to add to the classification. You could code these for ease of reference, giving each a colour or symbol, for example. Gift Hampers Wine Gifts Garden Gifts Pampering Gifts Gifts for the Home Jewellery Gifts Chocolate Gifts Photo Albums Champagne Gifts Leather Gifts Whisky Gifts Gifts to Drink Further on in this activity, you will need to calculate some percentages. Here is how (in case you’ve forgotten…). Calculating percentages Formula Number of products in the set of that type X 100 Total number of products in that set Example 2 chocolate gifts X 100 = 10% 20 products in male data set © 2007 www.teachit.co.uk 8057 Page 6 of 22
7.
Language, gender and
online shopping An investigation Type of product: male data set Name of product Type of product Name of product Type of product Leather Golf Ball The Scotch Whisky Holder, Golf Balls & Hamper Tees Pewter Tankard & Real Leather Gold Ball Ale Hamper Marker Pouch England or Scotland Aspirin Cufflinks Cufflinks Golf On The Go Hamper Happy Birthday Hamper Hunters Flask & Cups Mini Texas Hold ‘Em Poker Game Malt Whisky Cake Leather Driving Licence Wallet Macallan 10 Years Old ‘Worlds Best Dad’ Single Malt Whisky Chocolate Gift Leather Wash Bag The Scotch Whisky Hamper Bath and Body Essentials for Men Gift Box Banoffee Chocolates The Beer Game Golf or Fishing Hobby in a Box! Brandy Glass Warmer, Brandy Glass & Martell Cognac © 2007 www.teachit.co.uk 8057 Page 7 of 22
8.
Language, gender and
online shopping An investigation Type of product: female data set Name of product Type of product Name of product Type of product Make-up Bag & Organic ‘Hot Hunk’ Hot-Water Pamper Kit for Hands Bottle Diamond & Silver Open Belgian Chocolate Heart Necklace Hearts Leather Jewellery Roll Leather Travel Wallet Relaxing Lavender Bath Organic & Gorgeous Gift Box Weekender Gift Box Champagne & Chocolate Lilac Handblown Glass Truffles Scent Ball The Great Big Garden The Chocolate Passion Hamper Hamper ‘Forget Me Not’ Pocket Heart Hot-Water Bottle Garden Leather Bag for Make Up Bear Bearing Chocolates & Brushes The Afternoon Tea Bone China Tea for One Hamper Set Semi-precious Stones Chocolate Grand Necklace Marnier Cake ‘World’s Best Mum’ Breakfast Coffee or Chocolate Gift Afternoon Tea Gift Set Organic & Gorgeous Happy Birthday Hamper Scented Candle Gift Box French Hot Chocolate Chablis Premier Cru in Pot Gift Set Presentation Box Organic & Gorgeous Pampering Gift Box © 2007 www.teachit.co.uk 8057 Page 8 of 22
9.
Language, gender and
online shopping An investigation • How many types of product are there in the data set? Gifts for Men Gifts for Women • What percentage of the product descriptions in the data set belong to each product type? Product type % in male data set % in female data set Gift Hampers Garden Gifts Gifts for the Home Chocolate Gifts Champagne Gifts Whisky Gifts Wine Gifts Pampering Gifts Jewellery Gifts Photo Albums Leather Gifts Gifts to Drink © 2007 www.teachit.co.uk 8057 Page 9 of 22
10.
Language, gender and
online shopping An investigation TASK 2 Now compare ‘male’ and ‘female’ items of the same product type to determine if there are any gendered patterns. An example has been filled in to get you started, though feel free to amend/improve this (you may, for example, disagree with this classification of product types). Product Male data set Female data set Notes and questions type Gift Hampers Garden Gifts Gifts for the Home Chocolate Gifts © 2007 www.teachit.co.uk 8057 Page 10 of 22
11.
Language, gender and
online shopping An investigation Product Male data set Female data set Notes and questions type Champagne Gifts Whisky Gifts Wine Gifts Pampering 1) Bath & body essentials gift 1) Make-up bag & organic More items for women. Could suggest women Gifts box pamper kit for hands are airheads only interested in mindless 2) Relaxing lavender bath gift activity like ‘pampering’; or greater need for box relaxation due to the burden of modern 3) Organic & gorgeous women’s lives?? What does it suggest about pampering gift box men? Are these products still seen as an 4) Organic & gorgeous affront to masculine identity? weekender gift box Jewellery Gifts © 2007 www.teachit.co.uk 8057 Page 11 of 22
12.
Language, gender and
online shopping An investigation Product Male data set Female data set Notes and questions type Photo Albums Leather Gifts Gifts to Drink © 2007 www.teachit.co.uk 8057 Page 12 of 22
13.
Language, gender and
online shopping An investigation TASK 3 Follow the instructions in the separate document ‘How to generate a corpus gender keyword list’. Print off the two lists this creates, one for male keywords and one for female keywords. Alternatively, your teacher may provide you with these lists. Either way, read them carefully and get to know the data. You could print off or photocopy a couple of copies of each list so that you keep one clean ‘reading’ copy, and one ‘working’ copy you scribble thoughts and ideas over, colour code, and whatever else helps you get to know the data well. TASK 4 You now need to investigate the keyword lists for both data sets to explore interesting patterns of gender differences and to test these against key markers of language and gender according to popular beliefs and research ideas. A Keyword list can help you to see some things more clearly, but looking at the whole texts will reveal other interesting patterns and observations. Start by working through these activities, but don’t be afraid to experiment with other approaches and to explore other interesting lines of enquiry. 1. Semantic fields A popular idea about language and gender is pithily summarised in the popular self-help book title ‘Men are from Mars, Women are from Venus’. This suggests that men and women have completely different world views, and it is a popular belief that this reveals itself in male and female discourse in the predominance of different semantic fields. Test this by classifying each data set (male data set, and female data set) into semantically related groups of words. You could use the semantic fields diagram sheets on the next pages to help you with this. Examples are given to guide your thinking. © 2007 www.teachit.co.uk 8057 Page 13 of 22
14.
Language, gender and
online shopping An investigation Semantic fields diagram sheet Male data set Give each box the name of a significant semantic field for which you have identified at least three words in the keyword data set. You could write these in too, or list them separately. Add as many boxes as you need. SPORT & GAMES Poker, golf, tees Male data set © 2007 www.teachit.co.uk 8057 Page 14 of 22
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Language, gender and
online shopping An investigation Semantic fields diagram sheet Female data set Give each box the name of a significant semantic field for which you have identified at least three words in the data set. You could write these in too, or list them separately. Add as many boxes as you need. LUXURIOUS finest, organic, indulgent, Handmade Female data set 2. Pre-modification of nouns Another popular idea is that female discourse is more ‘flowery’ or more ‘descriptive’ than male discourse. It can be difficult to pinpoint what people mean when they say this, but for the purposes of this investigation we can focus on how the nouns in each data set (all gift items) are pre- modified. Start by sorting the words in each data set into their word classes. You may be given sets of cards with the male/female words on to make the process of sorting easier. You could use the word classes classification sheets on the next two pages to document this. © 2007 www.teachit.co.uk 8057 Page 15 of 22
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Language, gender and
online shopping An investigation Word classes classification sheet – male data set This is a traditional word classes classification. It is not the only approach and it has its limitations, not least in this activity because nouns and adverbs can pre-modify nouns in the same way that adjectives do. There are two sources of information that can help you identify the word class of a word: a good dictionary will help you narrow the range of options down, as a single word can belong to more than one word class (eg, in ‘take a walk’ walk is a noun, but it is a verb in ‘I walk to school’); looking back at the word in its original context(s) may clarify its specific use in the example(s) we are considering. This task will take detective work and judgement. Remember that you are not working with every word from the texts, only those that are significantly marked in contrast with the other data set. NOUNS ADJECTIVES VERBS ADVERBS CONJUNCTIONS PREPOSITIONS PRONOUNS DETERMINERS © 2007 www.teachit.co.uk 8057 Page 16 of 22
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Language, gender and
online shopping An investigation Word classes classification sheet – female data set This is a traditional word classes classification. It is not the only approach and it has its limitations, not least in this activity because nouns and adverbs can pre-modify nouns in the same way that adjectives do. There are two sources of information that can help you identify the word class of a word: a good dictionary will help you narrow the range of options down, as a single word can belong to more than one word class (e.g. in ‘take a walk’ walk is a noun, but it is a verb in ‘I walk to school’); looking back at the word in its original context(s) may clarify its specific use in the example(s) we are considering. This task will take detective work and judgement. Remember that you are not working with every word from the texts, only those that are significantly marked in contrast with the other data set. NOUNS ADJECTIVES VERBS ADVERBS CONJUNCTIONS PREPOSITIONS PRONOUNS DETERMINERS © 2007 www.teachit.co.uk 8057 Page 17 of 22
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Language, gender and
online shopping An investigation Thinking about noun pre-modification Then you need to check how the nouns are working. To do this you need to know a little bit about noun phrases and their structures. A noun phrase is the little cluster of words that form the subject or object of a sentence, the thing that is doing the verb or having it done to it in simple examples, or the thing or idea that is, seems, feels and so on in more complex examples. Here is a simple example: The dog barked The green highlighted text is a verb (technically, speaking a verb phrase because the verb ‘bit’ can be more than one word, e.g. is barking), the yellow highlighted text is a noun phrase. The noun phrase can be analysed further as it contains more than one word. The dog ‘dog’ is a noun and ‘the’ is a determiner. We could add more description to our noun phrase. The enormous dog ‘enormous’ is an adjective which pre-modifies the noun. ‘Pre’ means it comes before the noun, and ‘modifies’ means it changes our perception of it. We hit ‘enormous’ before we encounter ‘dog’ and our perception of the dog is shaped by the word that comes before it. We could add other words as pre-modifiers. More adjectives: The enormous white dog An adverb: The really enormous white dog Another noun: The really enormous white plastic dog If we need to, we can distinguish between the role the noun ‘dog’ is playing in this phrase, and the role the noun ‘plastic’ is playing here, by calling ‘dog’ the head noun and ‘plastic’ a premodifying noun. A verb form: The really enormous white plastic grinning dog Got that? When nouns appear as pre-modifiers they can tend to create a more factual style of description. Hold on to that thought. © 2007 www.teachit.co.uk 8057 Page 18 of 22
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Language, gender and
online shopping An investigation When adjectives appear as pre-modifiers they can create very different styles, depending on their nature. One way of classifying these is to divide them into these three types: Descriptive adjectives which give factual detail of the material property of an object e.g. size (‘large’), shape (‘round’), colour (‘blue’), texture (‘rough’). Evaluative adjectives which either state or imply a judgement of whether something is good or bad, e.g. ‘good’, ‘bad’, ‘ugly’, ‘beautiful’, ‘skinny’, ‘funny’. Emotive adjectives which express an emotional attitude, often adjectives that have been bleached of any very specific meaning, e.g. ‘fabulous’, ‘horrendous’, ‘amazing’. As with most attempts to classify language, it is difficult to apply this system ‘cleanly’ to every adjective, but it does give a starting point for stylistic analysis. A more factual style is more likely when a greater frequency of descriptive adjectives is used; a more judgemental or emotional style is more likely with the other two types of adjectives in greater frequency. It is a popular belief that male discourse is likely to be dominated by a more factual style and female discourse by a more emotional style. The linguist Robin Lakoff contributed to this view with the idea from her observational research that women use more ‘empty’ adjectives (largely the same as our ‘emotive’ classification) and also more ‘intensifiers’ (adverbs such as ‘really’). These are all issues to investigate in our gender data sets. © 2007 www.teachit.co.uk 8057 Page 19 of 22
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Language, gender and
online shopping An investigation Back to the data So, you need to work through all of the nouns in your two data sets, and find out whether they are operating as the head noun in the noun phrase, or whether they are pre-modifying a head noun. Here are two examples from the data sets. Example 1 steel Male data set steel might be regarded on the keyword list as a head noun but closer investigation in context reveals that this is used as a pre-modifier in noun phrases such as stainless steel flask (Hunters Flask & Cups), where flask is the head noun, premodified by stainless and steel Example 2 silk Female data set silk might be regarded on the keyword list as a head noun but closer investigation in context reveals that this is used as a pre-modifier in noun phrases such as exquisite silk packaging (Organic & Gorgeous Pampering Gift Box), where packaging is the head noun, premodified by exquisite and silk When you have sorted the nouns out, make a list of all the pre-modifiers in each data set. Then complete the pre-modifier classification sheet, amending or improving it as you see fit. © 2007 www.teachit.co.uk 8057 Page 20 of 22
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Language, gender and
online shopping An investigation Pre-modifier classification sheet Sort the pre-modifiers you have identified from the keyword lists into these categories. Feel free to amend/improve this approach. Male data set Female data set Comments and questions Nouns as pre- modifiers Adverbs as pre- modifiers Descriptive adjectives as pre- modifiers Evaluate adjectives as pre-modifiers Emotive adjectives as pre-modifiers © 2007 www.teachit.co.uk 8057 Page 21 of 22
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Language, gender and
online shopping An investigation Pre-modifier classification sheet – teachers’ notes This resource is deliberately ‘holey’. It includes classification of the data, but only some initial thinking points in the ‘comments and questions’ column. These are not exhaustive, the ideas need development, and they are contestable – the point is to show how the task gives rise to some interesting lines of enquiry. It is not a set of ‘answers’. Male data set Female data set Comments and questions Popular ideas about language and gender Nouns as pre-modifiers Malt would have it that male discourse will contain more nouns as pre-modifiers Popular ideas about language and gender Adverbs as pre- would have it that female discourse Really, two modifiers would include more frequent use of ‘really’ as an intensifying pre-modifier Verbs as pre-modifiers Relaxing, soothing Interesting difference in colour palettes; Stainless, steel, green, Scented, handmade, Descriptive adjectives interesting contrast between metallic metal, tan, black, lavender, long, pink, blue, as pre-modifiers fabrication in male data set and luxury complete, single, highland hot, organic fabrication in the female data Interesting contrast between Evaluative adjectives as Great, good, smart Indulgent, beautiful monosyllabic words in male data set and pre-modifiers polysyllabic words in female data set… Emotive adjectives as Fabulous, lovely, wonderful, Reinforces Lakoff’s assertion about pre-modifiers gorgeous ‘empty’ adjectives and female discourse? Not enough data to support a strong Superlative adjectives claim but perhaps in line with Lakoff’s Finest as pre-modifiers assertion about intensifiers and female discourse © 2007 www.teachit.co.uk 8057 Page 22 of 22
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