<b>Variation in Second and Heritage Languages:</b> Crosslinguistic Perspectives. Ed. by Robert Bayley, Dennis R. Preston, and Xiaoshi Li. (Studies In Language Variation 28.) Amsterdam: John Benjamins, 2022. Pp. 365. ISBN 9789027211149. $158 (Hb).
Bibliographic record
Abstract
Reviewed by: Variation in second and heritage languages: Crosslinguistic perspectives ed. by Robert Bayley, Dennis R. Preston, and Xiaoshi Li Elaine Tarone Variation in second and heritage languages: Crosslinguistic perspectives. Ed. by Robert Bayley, Dennis R. Preston, and Xiaoshi Li. (Studies in language variation 28.) Amsterdam: John Benjamins, 2022. Pp. 365. ISBN 9789027211149. $158 (Hb). This edited volume centers upon variability in second and heritage languages as a necessary attribute of all language development and change, focusing particularly on variability in learners' language that is primarily tied to linguistic context. As the editors and authors of the introductory chapter correctly state, the idea that the utterances produced by second language (L2) learners are variably influenced by both social and linguistic context is not new. They state that the intended contribution of this volume is to expand scholarship in three areas of research on developmental variability: (i) the number of second and heritage languages (HLs) and language varieties included in the study of variation in second language acquisition (SLA), (ii) users' perceptions of the social meanings of variable forms in L2s and HLs, and (iii) the use of mixed-effects linear regression models (MELMs) in measuring the significance of research findings on such variation. The book includes an introductory chapter and twelve research studies drawn from an impressive range of international locations and languages; the research studies are grouped by target language (that is, the language being acquired), namely: Mandarin, Korean, Cantonese, Spanish, French, Italian, and Catalan. Taken together, these studies produce important findings on the impact of linguistic context on a variety of features in the target language produced by L2 and HL learners. In Ch. 1, the editors (Robert Bayley, Dennis R. Preston, and Xiaoshi Li) introduce the three main goals of the book enumerated above and suggest the possible contributions of its studies to current variationist research on SLA. The first four studies examine the acquisition of Asian languages. Chs. 2 and 3 examine English speakers' acquisition of Mandarin Chinese as an L2: Ch. 2 by Li, Bayley, Xinye Zhang, and Yaqiong Cui focuses on the acquisition of the particle le, and Ch. 3 by Rebecca Lurie Starr examines the acquisition of Mandarin phonological variants in Singapore. The third study in this section (Ch. 4 by Mihi Park) turns to the acquisition of nominative argument realizations in Korean as a third language (L3) by bilingual Singaporean speakers of Mandarin Chinese and English, and the fourth (Ch. 5 by Holman Tse) examines the acquisition of Cantonese sociophonetics by English-speaking heritage learners in Toronto. The next three chapters focus on the acquisition of Spanish as an L2 or HL. The first (Ch. 6 by Chelsea Escalante and Robyn Wright) studies Spanish rhotic development by uninstructed speakers of English in Ecuador; the second (Ch. 7 by Kimberley L. Geeslin and Stephen Fafulas) is a quasi-longitudinal study of progressive and habitual verb marking by instructed speakers of English in the US; and the third (Ch. 8 by Rebecca Pozzi) covers Americans' development of sociolinguistic competence during study abroad in Buenos Aires. The next three chapters examine the acquisition of French as an L2. Ch. 9 (by Katherine Rehner, Raymond Mougeon, and Françoise Mougeon) focuses on French first language (L1) and L2 speakers' variable choice of prepositions with place names in Ontario. Ch. 10 (by Vera Regan) reports on ne deletion produced variably by Polish migrants to France in relation to topic, speaker identity, and language attitudes, and Ch. 11 (by Kristen Kennedy Terry) examines English speakers' variable schwa deletion in clitics, after different periods of study abroad in different social networks in France. The last two chapters focus respectively on the acquisition of Italian and of Catalan. Ch. 12 (by Margherita Di Salvo and Naomi Nagy) compares variable object marking in Italian as an HL by three generations of immigrants in Toronto with that of native speakers in Calabria, Italy...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".