Bibliographic record
Abstract
This paper critically evaluates the conventional insistence on establishing measurement invariance (MI) in cross-cultural psychology. We argue that complex and seemingly arbitrary benchmarks for assessing MI can be unrealistic and effectively prohibit meaningful research. The widespread use of various MI criteria creates unnecessary and often unattainable hurdles for cross-cultural researchers who have made the effort to collect data in multiple cultural contexts. Additionally, the prohibitionist tone of discussions surrounding MI is unhelpful, unscientific, and discouraging. We argue that emerging findings that cultural differences might not be as widespread or profound as once assumed imply that significant cross-cultural differences in measurement should not be the default assumption. Additionally, we advocate a shift towards external validity as a more useful metric of measurement quality. Our overall message is that researchers who go to the considerable trouble of gathering data in more than one country should not be disadvantaged compared to researchers who avoid cross-cultural complications by gathering data only at their home campus.
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 | 0.367 | 0.538 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".