Bibliographic record
Abstract
This volume of Onoma is themed "American Onomastics," and in our CFP, my co-guest editors and I described the theme as the study of names found anywhere in the Western Hemisphere, or as any study of names by scholars from this part of the world.We received nineteen proposals, encouraged fourteen to submit first drafts, and have chosen eleven to include in this volume.We have made no effort to showcase research representing any particular country, region, type of topic, or method of analysis.Many countries in our part of the world lack representation here, even though we know of significant research and scholars (e.g., in Canada).However, the scholars presented here answered our call, and their articles have been praised by at least two independent reviewers.It may be observed that three of the authors come from outside the Western Hemisphere, and yet their research focuses on names somewhere in this region.Thus, the diverse nationalities of these eleven authors illustrate the increasing reach of research enabled by the Internet.As presented here, these articles fall into four general categories of onomastic research distinguished by the types of things named: Anthroponomastics, Toponomastics, Zoonomastics, and Ergonomastics.Not surprisingly, most of the articles (6) are about anthroponyms, the names of people or groups of people.Three articles are about toponyms, commonly referred to as place names.One article is about pet names (the field of zoonymy), and one is about a product name (the name of a major league baseball team).No article is included here in the field of Literary Onomastics, even though we know of current work in this area.All of the articles reflect the dynamic relationship between names and culture.Grammatically speaking, the common nouns of a language refer (as signs) to categories of things understood within a culture, and names refer to specific items understood by individuals and/or groups within a culture.While my friends and I may understand the name Fido to refer to a specific dog, we also understand Fido to be among the things generally understood as dogs.So too the names of people, places, pets, and products identify specific items that are familiar to others within a specific cultural context.That is to say, people give names, and so names are rooted in cultural contexts in which the people live.Thus, culture affects names, and names reflect culture.This relationship is especially clear in our first article.Yolanda Lpez Franco and Rosales Novoa show how the patterns of baby names have changed differently in two Spanish-speaking communities, Santiago, Cuba and Tlalnepantla, Mexico.As the cultures of these communities have changed, the patterns of baby names have also changed, and they have changed in different ways.Traditionally, Spanish baby names have come from the names of saints
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
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".