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Record W2339288484 · doi:10.5539/hes.v6n2p109

The Usage of CAUSE in Three Branches of Science

2016· article· en· W2339288484 on OpenAlexvenueno aff
Bei Yang, Бин Чэн

Bibliographic record

VenueHigher Education Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
FundersNatural Science Foundation of Guangdong Province
KeywordsProsodyRegister (sociolinguistics)PsychologyLinguisticsMeaning (existential)VerbNatural (archaeology)Subject (documents)Computer scienceHistoryPhilosophyWorld Wide Web

Abstract

fetched live from OpenAlex

<p>Semantic prosody is a concept that has been subject to considerable criticism and debate. One big concern is to what extent semantic prosody is domain or register-related. Previous studies reach the agreement that CAUSE has an overwhelmingly negative meaning in general English. Its semantic prosody remains controversial in academic writing, however, because of the size and register of the corpus used in different studies. In order to minimize the role that corpus choice has to play in determining the research findings, this paper uses sub-corpora from the British National Corpus to investigate the usage of CAUSE in different types of scientific writing. The results show that the occurrence of CAUSE is the highest in social science, less frequent in applied science, and the lowest in natural and pure science. Its semantic prosody is overwhelmingly negative in social science and applied science, and mainly neutral in natural and pure science. It seems that the verb CAUSE lacks its normal negative semantic prosody in contexts that do not refer to human beings. The implications of the findings for language learning are also discussed.</p>

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.416
Teacher spread0.345 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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