MétaCan
Menu
Back to cohort
Record W4407232814

Enclitics in Croatian Language Teaching – Between Ideas and Practice

2013· article· en· W4407232814 on OpenAlexaff
Ana Kedveš, Ana Werkmann

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCroatianLinguisticsMathematics educationComputer scienceSociologyPsychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This paper deals with the position of enclitics in the context of Croatian language teaching in secondary schools. Given that the written corpus shows considerable deviation from the standard interpretation of the Wackernagel’s law (Peti-Stantić 2007), our intention was to explore this phenomenon in the context of the Croatian language teachers’ attitude towards the rules of enclitics’ placement. The previous works in this field show different aspirations. While most contemporary grammarians (Babić et al. 2007, Barić et al. 2005, Težak, Babić 1994) favour the prosodic interpretation or Wackernagel’s law, according to which enclitics must follow the first stressed word in the sentence, some contemporary linguists (Peti-Stantić) advocate syntactic interpretation of this rule in Croatian language, which entails the enclitics being placed after the first stressed syntactic unit. The latter base their claims on the discrepancy between the prescribed norms and actual language use. Our work addresses precisely that issue – the relationship between the nominal and practical elements in teaching the Wackernagel’s law. The analysis of research results indicates that teachers are inclined towards prosodic interpretation of Wackernagel’s law but are not likely to correct their students when they practise it differently.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.239
GPT teacher head0.528
Teacher spread0.289 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicLinguistics, Language Diversity, and IdentityFrench-language works237,207