Bibliographic record
Abstract
As the title of this chapter indicates, we will offer some introductory reflections on the contributions that comprise this edited volume on historical Ibero-Romance morpho-syntax.Concretely, we will first detail what connections they share and how they differ from each other.Subsequently, we will provide a detailed summary of its contents in order to guide the interested reader, before making some concluding observations.The common goal of the peer-reviewed contributions of the present volume is to contribute to the field of historical Ibero-Romance morpho-syntax.Several contributions develop fine-grained (micro-comparative) analyses on the basis of historical data ranging from the more widely spoken Ibero-Romance languages, such as Spanish and Portuguese (e.g., Schulte; Eide; Pountain; González Manzano; Moyna) to lesser-studied (but not less interesting) ones, such as Aragonese, Asturian and Catalan (e.g., O'Neill; Pérez Saldanya & Hualde).In the spirit of Maiden's (2004) recommendations for our field to thrive, several papers in this volume have also taken a macro-comparative approach and extend the scope of their investigation to other Romance languages (e.g., Rodríguez Molina & Enrique-Arias) and, of course, their Latin ancestor (e.g., Wright; Elvira; Rodríguez Molina & Enrique-Arias), while yet others highlight parallelisms and differences with other language families (e.g., Elvira; Melis & Flores; Rodríguez Molina & Enrique-Arias).Without a doubt, the biggest asset of the present volume lies in the richness of data that it provides: every single contribution, regardless of the theoretical framework they adhere to or advocate for, sprouts from the unequivocal respect for historical data, which has, in fact, led some authors to the dark corners of insufficiently explored
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.162 | 0.080 |
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".