Bibliographic record
Abstract
Purpose – The purpose of this paper is to illustrate the nature and scope of diversity between and within Asian countries. Design/methodology/approach – This paper represents a personal retrospective on the promise and perils of conducting research on Asia. Findings – “Promise” includes the growing research interest and attention on this region. “Perils” include, among others, a failure to recognize the diversity across countries in the region and within a given country. Immigration, rising incidence of bicultural or multicultural identity and brain circulation have all contributed to growing diversity within countries. Future research on this region should take into consideration such intra-national diversity. Originality/value – Although Asia’s “foreignness” may differentiate it from other regions around the world and, in doing so, contribute to the perception of its homogeneity, the region is considerably more diverse than what it appears to be. Thus, it is imperative to consciously recognize – and incorporate – diversity in a region of growing global importance.
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 | 0.128 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.018 | 0.043 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".