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Record W1588239581 · doi:10.14264/158129

Acquisition of word order in Chinese as a foreign language: An error taxonomy

2006· dissertation· en· W1588239581 on OpenAlex

Why this work is in the frame

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe University of Queensland · 2006
Typedissertation
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsnot available
Fundersnot available
KeywordsWord orderLinguisticsSecond-language acquisitionComputer scienceSentenceNatural language processingVerbChinese as a foreign languageForeign languageTaxonomy (biology)Artificial intelligence

Abstract

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Research in the field of Chinese second/foreign language (L2) acquisition, at present, does not match the increasing demand to learn Chinese as an L2, given that Chinese is the fastest growing foreign language (FL) in countries such as Japan, South Korea, the United States, Canada and Australia. There is a significant gap between Chinese L2 acquisition research and the large body of literature in second language acquisition (SLA), which mainly focuses on English L2. The need for more research in Chinese SLA is compelling.Particularly, research in Chinese L2 word order acquisition requires more attention because word order plays a more complex role in Chinese than in English. Chinese relies heavily on word order for information structuring of a sentence because this language lacks other means, such as verb endings indicating tense and aspect, to accomplish this function. Due to the different roles word order plays in Chinese and English, adult English-speaking learners find Chinese word order acquisition very challenging. Chinese L2 word order errors frequently occur in learners' L2 production. However, Chinese L2 researchers and teachers are left with no means to adequately describe and explain these errors for instruction purposes. This dissertation develops such a means -- a comprehensive taxonomy of Chinese L2 word order errors. This taxonomy organizes these errors into a logical system of classification. Through the classification, explicit description of various Chinese L2 word order errors is achieved, and specific sources of these errors are traced.Data was collected from 116 native-English-speaking learners of Chinese at a large university in Australia. The Chinese L2 learners were divided into three proficiency levels based on their institutional status. Four hundred and eight word order errors were extracted by qualitatively analyzing the learners’ written samples. Among the 408 word order errors, 404 (99%) are successfully classified into different categories according to a new criterion proposed in this dissertation.The new taxonomy provides a principle-based description and explanation of various Chinese L2 word order errors. A word order error is deemed to constitute an error when it violates a relevant word order principle (or sub-principle). These principles not only explain why an error is an error but also provide a means for correcting the error. In a pedagogical sense, the directness and explicitness in explaining word order errors achieved by employing this taxonomy cannot be achieved by relying on any other sources of errors available in the literature.The new taxonomy overcomes the limitations of existing taxonomies in the literature that are either superficial, or unsystematic, or not empirically testable. For example, it draws on the Cognitive Functionalist Approach of L2 acquisition. Both its description and explanation of Chinese L2 word errors go beyond superficiality. The approach maintains that adult L2 learners' conceptualization of the world is initially based on their L1. Their conceptualization of the world imposes constraints on the linguistic structures of their L2. Therefore, errors may occur when English learners of Chinese impose their conceptualization based on the English language onto the Chinese structures. The new taxonomy is systematic because it categorizes word order errors using one criterion. New categories emerging from the data and the existing categories from the literature are incorporated into one system. Finally, the new taxonomy is empirically testable because many new categories emerged from the data. It is an open-ended rather than a closed system. New categories can be added as necessary.The dissertation finds that violation of relevant word order principles has a high explanatory value for the various word order errors encountered in the data. This has clear pedagogical implications. Chinese L2 learners generally lack awareness of the word order principles (and sub-principles) on which the new taxonomy is based. These principles and sub-principles are seen to be of considerable importance to the acquisition of Chinese L2 word order. In order to improve learners' word order performance, the results of this study indicate that it is imperative for the basic Chinese word order principles be included in a CFL curriculum.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.238
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it