3.- PISOS TUTELADOS
ANÁLISIS DE LAS SOLICITUDES:
This facet separates fictional names from those that are real in the world and culture of author and reader. It reflects whether or not the referents of the names exist as entities outside the novel. It complements the scope facet and together they map the environment of the name and their relationship with the real world.
There are three categories within this facet:
- Realis – referent real - Irrealis – referent not real
- Mythical – referring to mythological or spiritual entities (the antichrist, Heaven, etc.)
The names of characters, both those internal to the novel and those external that happen to be mentioned in the stories, (e.g. Homer Simpson, Mr Darcy, Ally McBeal, etc.) are assigned the category ‘irrealis’. These names are considered irrealis on the basis that, although present in the world outside the novel (and therefore external in the scope facet), they are not real as tangible objects. The combination irrealis/external only applies to names of invented characters from other works of art
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outside the novel such as books, films, TV programmes, cartoons, even pictures. This may be more clearly visualized with an example: for instance, the name Doctor Who, when it refers to the TV series, would be realis and external – as a real entity in the world outside the novel; whereas, on the other hand, the name Doctor Who, referring to the character, would also be external but irrealis – as it does not exist in the real world.
The need to mark this distinction became apparent during the assignation of a category from each facet to each name (see 7.3 below). The benefits from marking such differences within a classification of the names quickly transpire as the most valuable thing to do, as it stresses potential fields which could affect the translation process. With a simple look at the complete classification of the name across all facets it will be possible to view at a glance the various aspects involved in the name, and this is the beauty of a faceted taxonomy; maybe each facet individually comes across as incomplete, but it is in the combination of all the facets where the true nature of each name is depicted. Much richer than uni-dimensional or hierarchic classifications.
7.3 Methodology
A total of 1,610 distinct proper names were identified in the English STs in BRIDGET. The first approach to the analysis of the data consisted in classifying all the names, with a view to offering a structure to the data and a clear terminology to refer back to during the other stages of the analysis process. The opening sections of this
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chapter described in detail the taxonomy created to undertake this classification of the data. In the following section I will move to describing the methodology used for applying this taxonomy and present some conclusions from the outcome of the process.
As indicated in the opening statements of this chapter, three Excel documents, one per book in the corpus, collected all the names extracted from each novel. These were arranged in alphabetical order, according to the SL texts. Each of these documents included a sheet (called “class”) with three individual columns, the first one to the left with the English names, the middle one with the corresponding Spanish translations and the third one to the right with the corresponding Italian translations. Five more columns were added for each language, one per facet in the taxonomy, with the exception of the ontological facet which had two columns in order to cover both for hypertypes and types. The final document has one column with the SL names, followed by five columns for hypertypes, types, semantic facet, sphere facet and scope facet (figure 7.9). A column with the Spanish translations then follows with five more columns for the taxonomy (figure 7.10) and then the same for Italian (figure 7.11), first the Italian names then space for the taxonomy.
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Figure 7.9: Excerpt from Amanda’s Wedding spreadsheet for classifying SL names.
Figure 7.10: Excerpt from Amanda’s Wedding spreadsheet for classifying TL names – SP.
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The entire document can be viewed in the appendixes included in the enclosed CD. The amount of data included in each sheet of these Excel documents make it impossible to fit in a single screenshot or impractical to print legibly.
Pick lists were created for each column with the corresponding categories for each facet, for instance, each cell in the semantic facet column has a drop down menu with three possibilities: primarily denotative, primarily connotative and intermediate. The same was created under each facet, with their corresponding categories. This facilitates the selection of one category per name under each section. This was replicated for all three languages. Simultaneously, four additional sheets (one per language – named count en, count sp, count it-, plus one more for the totals – named overall count-) were added to the spreadsheet, with an aim to collecting all the quantitative information from the classification performed on the original list. Conditional and mathematical formulas (COUNT IF and SUM) link each cell and each pick list in the first sheet with the corresponding sheet for each language making it possible to automatically account for every instance of each category. In other words, every time one category from the taxonomy is selected in the pick cell, it is counted for and automatically added to the corresponding cell for the total for that category. In this way it is possible to count the use of each individual type of name, within each language as well as in total. The idea behind this was to observe whether any changes in the category of the names were visible and due to the translation process. Systematically automating the counting makes it less prone to human errors in the count down and allows an easier manipulation of the data.
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Using the multilingual concordancer, a search for each name in the list was performed. Looking at the individual contexts, a category from each pick list was assigned to each name, both in the originals and the translations.
In the first instance, the ontological facet included the categories extracted from the Prolex classification. However, as seen in previous sections of this chapter, finding categories to match each type of name in BRIDGET proved difficult and the Prolex classification was consequentially adapted to the specific names in the lists (explained in section 7.2.2.1). The differences in both classifications have been explored above and illustrated in table 7.4b. Fine tuning of the other facets also took place following a first run through the data and taxonomy, for example, the decision to class all the names of invented characters as irrealis (7.2.4) took place at this stage.
The results from this process remarkably show that no changes to the category of name are carried out during the translation process. Basically, all the same categories are maintained throughout both in SL names and TLs names. Therefore, one conclusion already emerging is that regardless of any translation procedure employed to transfer proper names from SL texts to TL texts, these procedures do not alter the type of name. To a certain extent, this was to be expected within the ontological facet, that is, for instance, the names of the characters would of course, remain anthroponyms in the translations. And the same could be expected to apply to the toponyms, ergonyms, and so on. But it is somewhat more unexpected with regards to the other facets in the taxonomy, primarily the semantic facet. Still, no
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visible difference was appreciated. It remains to be seen, if the different categories bare any weight with regards to the translation procedures used (Chapter 9).
One last step was taken in order to ensure that this system of categories could be used with a certain degree of reliability (reliability here understood as “the attribute of data to stand in place of phenomena that are distinct, unambiguous, and real” (Krippendorff, 2008:356)). Content analysis involves testing a given set of categories in order to ensure that these categories are reliable and thus that the results generated by them can be confidently used in other analysis.
An experiment of the inter-annotator agreement was conducted with these data as the means to test its reliability. A sample of 100 names from Amanda’s Wedding were randomly chosen and classified again. The time span between the first classification and the second was considerable, and the original classification was removed while the work was being undertaken, thus ensuring that the new annotation was pure enough to be considered valid for the purpose of this experiment. Scott’s π (Scott, 1955) was used to assess the level of reliability of the data.
In order to calculate this I first established the proportion for each of the facets where the second classification was the same as the first, this is referred to as the observed proportion of matches. In order to calculate Scott’s π from this you also need to assess the proportion that would be expected to be the same by chance. Scott (Scott, 1955) describes a method of doing this that takes into account the fact
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that all categories in the facet are not equally likely to be chosen. The formula he proposes is the sum of the squares of the individual proportions of each category. If Po is the observed proportion that match and Pe is the expected proportion that match then π can be calculated as follows:
π =
The tab ‘Inter-annotator agreement’ in the spreadsheet in Appendix 2 – Amanda’s Wedding contains the new classification and the calculations required to generate π. The values obtained in this experiment for π for each facet are as follows:
Facet Π Hypertype 0.89 Type 0.83 Semantic 0.82 Sphere 0.83 Scope 0.85
Krippendorf (ibid., 2004:241) suggests a value of π greater than 0.8 is required to ‘ensure the data under consideration are at least similarly interpretable by researchers’ (ibid.). All the values obtained are above that threshold therefore the experiment has shown that this system of categories is repeatable, at least by the same researcher over an extended gap period. Thus, the taxonomy has proven
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trustworthy and the classification can be used with confidence in the rest of the analysis of this thesis.
7.4 Conclusions
The opening sections of this chapter introduce the taxonomy classification devised to structure the data extracted from BRIDGET. This taxonomy takes a faceted form, following recent developments in information science classifications. The categories within each facet were conceived from a combination of both existing classifications (for instance, that used by the group Prolex in their attempt to compile a multilingual dictionary of proper names), as well as the theories of meaning explored in Chapter 2. Section 7.3 described the methodology employed to apply the taxonomy to the names extracted from BRIDGET and explores its potential and the information that can be inferred from its application to BRIDGET.
Overall, the model and process devised for classifying the data proved both usable and useful. The multilingual concordancer (described in section 6.4.2.1) made it feasible to locate the names in all texts (STs and TTs) at great speed, and the pick lists and formulas automated the quantification of the figures, ensuring an error-free account of the use of each category. The final spread sheet (Appendixes 2, 3, 4), with all the names classified can be manipulated as desired in order to filter the names as needed, making it possible, for instance, to locate and group all the anthroponyms together, or all the connotative, irrealis toponyms, for instance. This offers great flexibility, and ensures clarity and efficiency in the results. All these
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features will prove useful in the incoming stages of the data analysis, as described in Chapters 8 and 9 to follow.
Furthermore, the inter-annotator agreement experiment conducted showed that the categories can reliably be used and trusted, thus strengthening the value of this taxonomy.
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