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Record W4210791605 · doi:10.1515/tlr-2021-2081

Differential subject marking through SE

2022· article· en· W4210791605 on OpenAlexaff
Virginia Hill, Monica Alexandrina Irimia

Bibliographic record

VenueThe Linguistic Review · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLinguisticsRomance languagesVerbSubject (documents)Object (grammar)Argument (complex analysis)RomanianCliticOpposition (politics)ReflexivityRule-based machine translationPronounComputer scienceMathematicsPhilosophySociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract An outstanding question in current studies concerns the status of Romance SE that does not obviously mark reflexivity or anticausativity. This paper signals the presence of such constructions in Old and Modern Romanian, where SE occurs with unergative verbs and qualifies as pleonastic according to traditional grammars (i.e., it makes no difference for the truth conditions or for the argument structure). The main argument is that such constructions are actually instances of differential subject marking (DSM) in Romanian, and that the semantic triggers and the underlying configuration resemble those that occur with differential object marking (DOM) in this language. In terms of theoretical contribution, this analysis (i) widens the cross-linguistic inventory of DSM patterns, by adding Clitic Doubling; (ii) confirms the predictions of recent studies that there could be similarity rather than opposition between DOM and DSM contexts; (iii) shows the possibility of re-allocating the reflexive pronoun SE to other configurations besides (an instance of) verb reflexivization.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.006
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.066
GPT teacher head0.290
Teacher spread0.224 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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