MétaCan
Menu
Back to cohort
Record W2482638226 · doi:10.1075/slcs.158.05bar

Chapter 5. A data-driven analysis of the structure type ‘man–nature relationship’ in Romanian

2014· book-chapter· en· W2482638226 on OpenAlexaboutno aff
Ana-Maria Barbu

Bibliographic record

VenueStudies in language companion series · 2014
Typebook-chapter
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRomanianType (biology)GeographyLinguisticsPhilosophyGeologyPaleontology

Abstract

fetched live from OpenAlex

The Romanian tri-nominal juxtaposition structure relaÈ›ie om – natură ‘man-nature relationship’ is cross-linguistically widespread and typical of a series of relational nouns, such as agreement , interaction , and mixture , which can have a “compound” expansion ( Canada – U.S. agreement, parent – child interaction, air – water mixture, etc.). Our analysis is twofold: we first examine the grammatical relationship between relaÈ›ie ‘relationship’ and om – natură ‘man–nature’, and second the construction om – natură . On the basis of data from a large Romanian newspaper corpus, we show that the “compound” construction om – natură is in fact a free phrase; we call it a Relational Coordination Construction (RCC). It usually embodies valency complements of a relational noun, and it semantically implies reciprocity. The analysis adopts a non-transformational, data-driven perspective within the Construction Grammar framework.

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 categoriesMeta-epidemiology (narrow)
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 score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0000.001
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 head0.042
GPT teacher head0.331
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 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueStudies in language companion seriesSame topicNatural Language Processing TechniquesFrench-language works237,207