On the Effect of Reciprocal Dyadic Relations on the Share of Lexical Practices
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Variationist studies have shown the implication of tie properties in the emergence and preservation of linguistic norms. This contribution deepens the understanding of this mechanism at the dyadic level. It explores relational subjectivity and relativity among individuals of a community and their implications in the distribution of lexical variants. The aim is to understand how the reciprocity of a relation influences the share of lexical practices. To do so, we analyze the network of discussions of bachelor's degree students of the University of Geneva and their lexical practices. Using the modern methods used in social network analysis to study relational properties and by running multiple regression quadratic assignment procedure (MRQAP), reciprocal interactions are found to lead to a higher lexical share and similarity.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it