Bibliometric analysis of willingness to communicate in the English as a second language (ESL) context
Bibliographic record
Abstract
Abstract Willingness to communicate (WTC) is an individual’s predisposition to communicate with a person or persons at a specific time. Over the past century, there has been a dramatic increase in WTC research. This study aims to offer an overview of the existing literature regarding WTC from 1900 to 2022 and provide a bibliometric and visual analysis of the research status as well as the development trend of this research field. A total of 428 journal articles were retrieved for the purpose of conducting bibliometric research. The data for this study were collected from the Web of Science (WOS). The results established the development status of the field of WTC, the annual scientific production and growth rate, the thematic evolution and trend topic, co-citation, and coupling between authors, sources, and countries. The bibliometric analysis showed that: (1) In the past decades, research in WTC has continued to soar and annual publications can be divided into three stages, which are an initial stage, a slow development stage, and a rapid expansion stage. It has been discussed to a wider extent mainly within the fields of education and linguistics; (2) of the 38 countries that the articles were exported from, the United States topped the list with the most publications, and Canada received the most citations. China has the most inter-country collaboration compared to other countries, which is at the center of international cooperation. China’s main cooperation countries were Iran, Japan, Canada, and Australia. The top author in the WTC field with the most production and impact is MacIntyre, PD, while System was the most popular journal. (3) By means of keyword analysis, “second language” was the most frequent keyword, followed by “model” and then “attitude”. Based on the results of the thematic evolution analysis, the research themes for 2021 to 2022 are “model”, “competence”, “Chinese”, “abroad”, “teachers”. The findings may be beneficial to L2 teachers and learners better to understand the role of WTC in language study. Researchers in this field might find the study useful for finding new research directions, relevant sources, and opportunities for collaboration.
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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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.096 | 0.134 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".