Bibliographic record
Abstract
This book offers an in-depth exploration of contemporary issues and methodologies in the fields of dialectology and sociolinguistics. Readers will find a diverse collection of studies that examine how language varies and changes across different regions, communities, and social contexts. The book covers a wide range of languages, including German, English, Yiddish, Russian, and Japanese, providing a global perspective on linguistic diversity. Key themes include the use of modern data sources, such as social media, to study language patterns and the impact of digital communication on regional dialects. The book also addresses the dynamics of language contact in expatriate communities, revealing how speakers adapt and merge linguistic features from different dialects. Several chapters focus on the evolution of dialectological research, offering critiques and new approaches to studying regional language variations. Readers will also encounter innovative methods, such as cognitive geography, which uses mental representations of space to understand dialect variation, and tone distance measures, which are crucial for studying tonal languages. Additionally, the book presents case studies on how non-experts perceive and categorize dialects, providing insights into the public's understanding of linguistic diversity. It also tackles challenges in selecting dialect speakers for research, especially in urban environments, where traditional criteria may no longer apply. Overall, this book is a valuable resource for linguists, researchers, and anyone interested in the complex and ever-changing landscape of human language. It highlights the importance of adapting research methods to keep pace with the evolving nature of language and offers fresh perspectives on how we study and understand dialects and language variation.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.051 |
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".