Variety of pronunciation models in European and American teaching or (self-)learning manuals of pronunciation for non-native speakers of Spanish
Bibliographic record
Abstract
This paper analyses a corpus of Spanish pronunciation manuals published in Europe (Spain and Italy) and in the Americas (United States, Canada, and Brazil) from the 1970s onwards, which are aimed at second-language learners. The aim is to answer the following questions: Which pronunciation model is adopted in (self-)learning pronunciation manuals for non-native speakers of Spanish in Europe and America? Is it possible to observe a convergence towards a unique model or do these manuals reflect a plurality of different models? What is the role of the Castilian norm? Is it still the only reference model in Europe? Is it still viewed as a prestige model in non-Spanish speaking parts of the American continent, as it has been for a long time? Finally, what are the phonetic and phonological characteristics of the pronunciation norms employed in these manuals? The results of the analysis show that the manuals in the corpus reflect a plurality of different pronunciation models. The Castilian norm, which distinguishes between /θ/ and /s/, and in most manuals also between /ʎ/ and /ʝ/, still has an undisputed primary role in Europe. In America, by contrast, three basic models can be observed, namely a neutral American— which in its main features coincides with the Spanish of Latin American highlands—, the European one, and Buenos Aires Spanish. Moreover, it must be pointed out that in American manuals the European model is always an alternative to the neutral American one and it is never proposed as a unique reference standard. Brazilian manuals, on the other hand, represent an anomalous case due to the lack of a unique reference standard as the teaching model. In this case, the three mentioned reference models represent alternative options based on characteristics of different kinds, as discussed in the article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".