All That Glitters Is Not Gold: How Others’ Status Influences the Effect of Power Distance Belief on Status Consumption
Bibliographic record
Abstract
This research proposes the relationship between power distance belief (PDB) and status consumption is moderated by the salience of others and their associated status (others’ status). When others’ status is not superior (similar or inferior), high-PDB consumers are more likely to engage in status consumption than low-PDB consumers. However, when others’ status is superior, high-PDB consumers are less likely to engage in status consumption. Both signaling effectiveness and need for status underlie the effect of PDB on status consumption. Need for status mediates the effect of PDB only when others’ status is not superior, whereas signaling effectiveness mediates the effect of PDB on status consumption when others’ status is superior, similar, or inferior. Compared to low-PDB consumers, high-PDB consumers perceive greater signaling effectiveness when others’ status is inferior or similar, but they perceive less signaling effectiveness, and therefore engage in less status consumption, when others’ status is superior. When status goods are consumed in private, and therefore not effective at signaling status, the interaction of others’ status and PDB is mitigated. This research articulates the nuanced effect of PDB on status consumption depending on others’ status as well as the multiple mechanisms underlying status consumption.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".