Corrigendum: Editorial: Stereotypes and Intercultural Relations: Interdisciplinary Integration, New Approaches, and New Contexts
Bibliographic record
Abstract
In the published article, there was an error in Author Contributions and Acknowledgments. Author Contributions should be “DG wrote the first draft of this paper. JB and AZ reviewed and edited the draft to finalizing it. All the authors approved the submitted version of this paper.”, whereas Acknowledgments should be “Dmitry Grigoryev conceived of the idea and coordinated this Research Topic. John W. Berry, Dmitry Grigoryev, Lusine Grigoryan, Anastassia Zabrodskaja and Susan T. Fiske contributed to an initial application on this Research Topic and the editorial process. The Editors thank all the authors and reviewers for their efforts and diligent work in finalizing this Research Topic.”. The original article has been updated.
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 | 0.007 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".