Deriving coordinate nouns with Merge and Principles of efficient computation
Bibliographic record
Abstract
We analyze coordinate nouns in English and derive their properties from Merge and Principles of efficient computation. The proposed analysis relies on extended projections for the coordinate conjunction and provides derivations to the interfaces with consequences for the externalization of the coordinator and the semantic interpretation of the coordinate nouns. The proposed analysis challenges associative theories of learning. It also challenges the view that apparently simplex forms, two-words expressions, are remnants of a previous stage in the evolution of language.-----------------------------------------------------------------------------DERIVANDO NOMES COORDENADOS COM MERGE E PRINCÍPIOS DE COMPUTAÇÃO EFICIENTEAnalisamos nomes coordenados em inglês e derivamos suas propriedades a partir de Merge e de Princípios de computação eficiente. A análise proposta baseia-se em projeções estendidas para as conjunções coordenadas e fornece derivações para as interfaces com consequências para a externalização do coordenador e a interpretação semântica dos nomes coordenados. A análise proposta desafia as teorias associativas de aprendizagem. Também desafia a visão de que formas aparentemente simples, expressões de duas palavras, são resquícios de um estágio anterior na evolução da linguagem.---Original em inglês.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".