Mapping literature of multicultural education: a bibliometric review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<span lang="EN-US">The value of multicultural education is acknowledged on a global scale, despite the fact that various barriers prevent its complete implementation. These include cultural, linguistic, religious, economic, difference in physical condition, and ethnic backgrounds. By assessing publishing trends, extracting data on author keyword trends, examining conceptual evolution, and establishing possible future research on this topic using the Scopus database. This study found that publications grew in quantity from 2013 to 2022, decreased in 2015 and 2018 but were not significant, and 2021 was the highest peak with 221 documents. With 111 publications, the United States is the most prolific and co-authored with authors from Canada, China, United Kingdom, Germany, Australia and South Korea. Based on thematic evolution, ‘subspace-clustering’, ‘conversational-system’, ‘aortic-aneurysm’, ‘Bayesian-network-classifier’, are themes or topics that have recently developed. By utilizing these important terms, the study of multicultural education can be examined more thoroughly and more extensive in the future in order to learn new knowledge. In conclusion, this research has the potential to contextualize previous research on the topic and create an evidence-based practice paradigm for future studies grounded in science.</span>
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.
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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it