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Un fenómeno bien curioso: New methods for analyzing variable intensification across four dialects of Spain and Argentina

  • Matthew Kanwit EMAIL logo , Virginia Terán and Silvia Pisabarro Sarrió

Abstract

Empirical study of variation between the Spanish intensifiers bien ‘very’ and muy ‘very’ has received little attention. A recent exception is (Brown, Esther L. & Mayra Cortés-Torres. 2013. Puerto Rican intensifiers: Bien/muy variables. In Ana Maria Carvalho & Sara Beaudrie (eds.), Selected proceedings of the 6th Workshop on Spanish Sociolinguistics, 11–19. Somerville, MA: Cascadilla Proceedings Project), who considered the conditioning factors of the intensifiers, although the study’s interview data included low use of the variants in certain linguistic contexts. Accordingly, our contextualized preference task elicits greater token counts across contexts and extends intensifier research across dialects. In our analysis of the four dialects of Tarragona and Madrid, Spain, and Tucumán and Buenos Aires, Argentina, we test the descriptive claims that bien is selected at higher rates in Latin America than Spain and that monolingual speakers from Madrid select bien at lower rates than Spanish-Catalan bilinguals from Tarragona, as predicted by descriptive literature. Furthermore, we investigate whether possible differences in rates and predictors between one capital city variety and that of a smaller city are mirrored across our two capital city contexts. We surveyed 205 native speakers of Spanish via a 24-item contextualized preference task. Participants chose their preferred intensifier or indicated that both were acceptable. We manipulated three independent linguistic variables: adjective quality, verb type, and animacy, and we consider the social variables age, gender, and, in the case of Tarragona, home language. Overall, we extend research on intensifier variation through a more controlled experimental design and cross-dialectal comparison.

Acknowledgements

We are grateful to the Spaniards and Argentineans who participated in our study and to Tim Face and three anonymous reviews for their helpful comments and suggestions. All errors are solely ours.

Appendix 1: Additional sample items from the contextualized preference task with English translations

  1. Natalia se junta con su mejor amiga Karina después de un tiempo sin verse e intentan ponerse al día. Natalia le cuenta que su abuelo se volvió a casar después de mucho tiempo, aunque su familia no estaba de acuerdo. Comenta:

    1. Su nueva esposa es bien joven, solo tiene 25 años.

    2. Su nueva esposa es muy joven, solo tiene 25 años.

    3. Ambas son posibles.

    ‘Natalia meets with her best friend Karina after a while without seeing each other, and they try to bring each other up to date. Natalia tells her that her grandfather got married again after a long time, although her family disagreed. She comments:

    1. His new wife is very (bien) young, she’s only 25.

    2. His new wife is very (muy) young, she’s only 25.

    3. Both are possible.’

  1. Natalia continúa la historia y le cuenta a su amiga que el casamiento de su abuelo fue en la casa de su bisabuelo. Le dice:

    1. No me gusta ir a esa casa porque los muebles son antiguos, huele a polvo y la casa está siempre muy oscura.

    2. No me gusta ir a esa casa porque los muebles son antiguos, huele a polvo y la casa está siempre bien oscura.

    3. Ambas son posibles.

    ‘Natalia continues the story and tells her friend that her grandfather’s wedding was in her great grandfather’s house. She says to her:

    1. I don’t like to go to that house because the furniture is old, it smells like dust, and the house is always very (muy) dark.

    2. I don’t like to go to that house because the furniture is old, it smells like dust, and the house is always (bien) dark.

    3. Both are possible.’

Appendix 2: Confidence interval tables from the multinomial regressions

Table 14:

95 % Confidence Interval for Bien v. Muy.

GroupAdjective qualityVerb typeAnimacy
Var. -Var. +Invar. -Inv. +SerEstarOtherAnim.Inan.
LowerUpperLowerUpperLowerUpperBaseLowerUpperLowerUpperBaseLowerUpperBase
Tuc0.71.71.12.60.51.4Base0.30.80.61.3BaseNANABase
BA0.62.62.38.40.73.0Base0.72.10.92.8Base0.30.7Base
Tar0.63.61.15.60.53.0Base0.41.70.41.8BaseNANABase
Mad0.62.80.94.00.52.6Base0.30.90.20.6BaseNANABase
  1. Note: If the values for the upper and lower limits of the 95 % confidence interval contain one, then the category is not statistically different from the base category. If the upper and lower limits are greater than one, then the odds of choosing a category other than the base (i.e., bien or “both”) are greater than the odds of choosing the base (i.e., muy). If the values for the upper and lower limits are less than one, then the odds of selecting a category other than the base are less than the odds of selecting the base.

Table 15:

95 % Confidence Interval for “Both” v. Muy.

GroupAdjective qualityVerb typeAnimacy
Var. -Var. +Invar. -Inv. +SerEstarOtherAnim.Inan.
LowerUpperLowerUpperLowerUpperBaseLowerUpperLowerUpperBaseLowerUpperBase
Tuc0.41.10.71.60.41.1Base0.50.90.51.1BaseNANABase
BA0.81.91.84.20.71.9Base0.61.30.40.9Base0.61.1Base
Tar1.12.81.53.80.51.4Base0.71.50.30.8BaseNANABase
Mad0.82.21.43.50.51.5Base0.91.90.40.9BaseNANABase
Table 16:

95 % Confidence Interval for Bien v. Muy.

GroupAgeGenderHome language
18–3435–5455+FemaleMaleCatalanSpanishBoth
LowerUpperLowerUpperBaseLowerUpperBaseLowerUpperLowerUpperBase
Tuc0.41.10.71.6Base0.51.1Base
BA1.33.70.82.3Base0.51.2Base
Tar0.54.80.97.7Base0.10.8Base1.210.90.64.7Base
Mad0.31.70.00.5Base0.30.9Base
Table 17:

95 % Confidence Interval for “Both” v. Muy.

GroupAgeGenderHome language
18–3435–5455+FemaleMaleCatalanSpanishBoth
LowerUpperLowerUpperBaseLowerUpperBaseLowerUpperLowerUpperBase
Tuc1.13.10.82.1Base0.40.8Base
BA1.73.70.71.6Base1.12.3Base
Tar0.51.20.30.7Base0.20.4Base0.61.50.30.8Base
Mad1.36.90.64.7Base0.61.1Base

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Published Online: 2017-9-9
Published in Print: 2017-9-26

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