Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a conceptual framework that highlights the reinforcing nature of global consumer culture (GCC). In doing so, this paper highlights a dialectic process in which consumers trade-off, appropriate, indigenize and creolize consumption into multiple GCCs. Design/methodology/approach The approach is conceptual with illustrative examples. Findings GCC is a reinforcing process shaped by global culture flows, acculturation, deterritorialization, and cultural and geographic specific entities. This process allows consumers to indigenize GCC, and GCC to contemporaneously appropriate aspects from myriad localized cultures, producing creolized cultures. Research limitations/implications Marketing research and practices need to shift away from the dichotomous view of global and local consumption fueled by a misleading view of segmentation. Instead, marketers should focus on identifying the permutations of emerging GCCs, how these operate according to the context and accordingly position their marketing mix to accommodate them. Originality/value The proposed model reviews and integrates existing literature to highlight fundamental research directions that present a comprehensive overview of GCCs, its shortcomings and future directions.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".