Bibliometric of the 100-most-cited articles on refugees.
Bibliographic record
Abstract
Background: Bibliometrics is a form of quantitative analysis that employs peer-reviewed research, journal articles and citation counts to examine the content of current literature on a particular topic. The authors aim to identify the major academic disciplines that dominate the landscape of published materials and research endeavors on the topic of refugees. Methods: Using the Web of Science, a database of most-cited articles was created by a team with expertise in bibliometrics. Results: Citations ranged between 1,493 and 105; averaging 203 citations per article. The publications spanned the years from 1973 to 2010. The year 2004 had the highest number of publications. All articles were published by 45 journals. In total, 294 investigators authored these articles. Psychiatry, psychology and public health constituted the top three fields of affiliation, with the most investigated feature being the mental health of refugees. Single investigators authored a quarter of all articles. Conclusion: This bibliometric evaluation allowed a multi-dimensional outlook on the conditions of refugee populations across the globe, through collation of relevant peer-reviewed research journal articles. This specialized form of assessment has resulted in a multi-disciplinary compendium of publications on the subject.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.038 |
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".