Las palabras del miskito: EI diccionario Español-Miskito, Miskito-Español, compilado por Dionisio Melgara
Bibliographic record
Abstract
klungwaika (k1unhwaika)=parte por donde se magulla.klungwanka (k1unhwanka)=acci6n de k1ungwaia 0 k1unhwaia.y Honduras.La actua1izaci6n de 1a ortografia -que refleja el uso actual predominante en la RAAN-es de particular interes en vista de una posible evoluci6n en la pronunciaci6n del idioma con relaci6n al tiempo en que los diccionarios que sirven de fuente principal fueron elaborados.Los resultados de las investigaciones lexicograficas publicadas en Vaughan (59) y Heath & Marx (61), reunidos y enriquecidos por el trabajo del Dr. Melgara, son complementarias en varios sentidos.El principal de ellos es que Vaughan incluy6 mucha informaci6n sobre la morfologia derivativa, mientras que Heath y Marx consideraron esta informaci6n redundante.Comparemos: Vaughan incluye, junto con los verbos, los sustantivos derivados de las raices verbales.Heath y Marx solo incluyeron los verbos en infinitivo, dejando al usuario la tarea de derivar los sustantivos cada vez que esto puede hacerse de manera regular, de forma que tanto el significado como la conforrnacion de estas palabras derivadas son predecib1es (Heath es tambien el autor de una excelente gramatica del miskito, donde los procesos derivativos regulares son explicitadosver referencias).k1ungwaia; pelarse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".