Replication Data for: Diverging grammaticalization patterns across Spanish varieties: the case of 'perdón' in Mexican and Peninsular Spanish
Bibliographic record
Abstract
Dataset description: This dataset contains one data file (.csv) used to create the tables in the paper "Diverging grammaticalization patterns across Spanish varieties: the case of perdón in Mexican and Peninsular Spanish". The data used to investigate the contemporary uses of the apology marker perdón come from a sizeable, manually annotated corpus consisting of spoken spontaneous conversations and interviews, recorded during the last quarter of the 20th century and the first decades of the 21st century. The following list of existing, spoken corpora were exploited: Corpus del Proyecto para el Estudio Sociolingüístico del Español de España y de América (PRESEEA), América y España español coloquial (AMERESCO), Corpus Sociolingüístico de la Ciudad de México (CSCM), Corpus del Habla de Baja California (CHBC) and Corpus Michoacano del Español (CME) for Mexican Spanish and Corpus Oral de Madrid (CORMA), Valencia Español Coloquial (Val.Es.Co.), Corpus Oral de Referencia del Español Contemporáneo (CORLEC), Corpus integrado de referencia en lenguas romances (C-ORAL-ROM) and Corpus del Proyecto para el Estudio Sociolingüístico del Español de España y de América (PRESEEA) for Peninsular Spanish. The data file includes all occurrences of perdón ('sorry') together with its near-synonymous apologetic markers such as lo siento ('I am sorry') and forms derived from the performative verbs perdonar ('to forgive') and disculpar ('to apologize'). It contains a total of 769 occurrences: 363 cases for Mexican Spanish and 406 for Peninsular Spanish. The data is annotated for (i) Spanish variety, (ii) corpus, (iii) form, (iv) type of offense, (v) face affected (positive face/negative face) and (vi) orientation of the face (speaker/hearer).
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.028 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".