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Record W4214693875 · doi:10.5539/ijel.v12n2p78

An Empirical Study on the Omission of Third-Person Singular -s in Writing

2022· article· en· W4214693875 on OpenAlexvenueno aff
Hongbo Qi

Bibliographic record

VenueInternational Journal of English Linguistics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMorphemeMistakeGrammarThird personPsychologyChinaLinguisticsMathematics educationHistoryPhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Enlightened by studies in inflectional morpheme in L2 speech, this article reports a survey on the use of third-person singular -s in writing by 6 L1 Chinese L2 English learners enrolled in Lancaster University in 2014. The results show that these undergraduate students omitted almost half -s marking in obligatory contexts but made little or no mistake when they actually used third-person singular -s. The students were more successful in using be form than -s form. The grammar of these students seemed to be “fossilized” in -s marking since they made no improvement after attending a pre-sessional program at Lancaster University. Whiling echoing the findings of previous studies on either Chinese or other English learners (adults or Children), this article concludes that the acquisition third-person singular -s is a challenge for adult Chinese English learners in writing. The implications for English language teaching in China are also discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.340
Teacher spread0.278 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicEFL/ESL Teaching and LearningFrench-language works237,207