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Like, Yeah: Gendered “Like” Usage Among Stanford Students

Like, Yeah: Gendered “Like” Usage Among Stanford Students. Ryan Mead Takeo Rivera Tamarah Shuer Jess Zutz. The Silicon Valley Girl???. Like, the Big Picture:.

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Like, Yeah: Gendered “Like” Usage Among Stanford Students

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  1. Like, Yeah:Gendered “Like” Usage Among Stanford Students Ryan Mead Takeo Rivera Tamarah Shuer Jess Zutz

  2. The Silicon Valley Girl???

  3. Like, the Big Picture: • Among the Stanford student body, women and men use introductory discourse particles at roughly equal rates (men at about 22%, women at about 24%). • Women and men also tend to use the most common discourse particles (i.e. “so,” “but,” “well,” “I mean,” “maybe”), at roughly the same rates. • However, men are far less likely to use the “like” particle than their female counterparts. • This seems to indicate that “like” has a unique social meaning among discourse particles.

  4. So, like, what is a discourse particle? Eckert, P. and S. Mc-Connell-Ginet, Language and Gender. Cambridge University Press: 2004 • “They are sometimes said to be ‘empty’ or mere ‘verbal fillers…’(184).” Discourse particles generally do not contribute to content. • Discourse particles are often thought of as being “empty,” weakening the speaker’s position. But this is not always the case.

  5. Our Discourse Particles • Focused on only introductory discourse particles-- the first word in each utterance, rather than in the middle.

  6. General Discourse Particle Findings • 23.5% of utterances contained introductory discourse particles. • Is this frequent or infrequent? • Compare to 24.2% (198/817) of utterances contain introductory non-lexical hesitations.

  7. Our, Like, Methods • Sorted out all data with discourse particles (192 out of 817 tokens). • Compared male-to-female usage of each discourse particle, among total discourse particles by gender • Then compared/contrasted male-to-female usage of each discourse particle with weighted averages • Due to the fact that we only had 192 tokens to work with, the data set is extremely small, and results should not be taken as all-encompassing.

  8. Some findings… • Among most discourse particles (e.g. “so,” “but,” “well,” “I think”), men and women had nearly equivalent rates of usage. • Also, among total discourse particles, men and women use introductory discourse particles at the same frequency. • Men use discourse particles ≈22% of the time • Women use discourse particles ≈24% of the time

  9. Some MORE Findings… • While overall, men and women use discourse particles very similarly, there is one significant difference when analyzing the data: “like.” • “Like” usage among women was not exceptionally high, at about 15% of all discourse particles. • However, among men, “like” usage was exceptionally low, at about 3% of all discourse particles. • Therefore, “like” is significant not because women use it so frequently, but because men use it infrequently!

  10. So, like, clearly… “Like” has a different, gendered social meaning compared to its confederates!!!

  11. Broader social implications? • “Like” can index a variety of things; colloquially, we are familiar with its association with hesitation, unformed ideas, and lack of definiteness. • Does this mean that men’s lack of usage of “like” indicates a desire to avoid feminization, or lack of conviction or intellectual efficacy? • Does this mean that women’s willingness to use “like” indicates their greater security in reconciling their femininity and intellectual agency?

  12. Social Implications, cont’d • While “like” usage does appear to have a gendered meaning of some sort, its relation to femininity is ambiguous. • Most likely, “like” is perceived differently by each gender. • “Like,” in combination with other factors, does not necessarily tag femininity. • But, for reasons not entirely clear, “like” is fairly rare in the male community lexicon.

  13. Further Research Queries • With more data and/or time, these results could be more conclusive. • We could only collect tokens that contain discourse particles. • We could also code for “like” usage in the middle of sentences. • Furthermore, we could correlate with size of class, instructor gender, and gender proportions.

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