Bibliographic record
Abstract
Purpose The purpose of this paper is to comment on the article “Consumer cultural identity: Local and global cultural identities and measurement implications” by Yuliya Strizhakova and Robin Coulter. Design/methodology/approach The commentary summarizes the main characteristics of the authors’ study, positions it in the context of globalization, and suggests additional directions for potential future research. Findings The article by Strizhakova and Coulter has many strengths and provides a good base for new studies on consumer cultural identities and their global, local or glocal orientations. Originality/value This paper adds four points on the theme of “what else” might additional research in this area contribute: The need for further investigations into the cultural orientations of consumers in less developed countries; whether and how practitioners use the findings of academic research; the difficulties in absorbing and using the existing voluminous literature when designing new studies; and the benefits to be gained by introducing more granular perspectives in research about consumers’ cultural identities and their effects on their marketplace behaviour.
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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.041 | 0.049 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".