The consumer acculturative effect of state-subsidized spaces: spatial segregation, cultural integration, and consumer contestation
Bibliographic record
Abstract
Although extant consumer acculturation research has investigated the acculturative effect of various ideological and institutional contexts, it has devoted minimal attention to how spatial structural conditions, in particular state-subsidized spaces, affect the consumer acculturation experiences of poor immigrants. This study builds on spatial governmentality theory to investigate the creation and consumption of a racialized gated community in Italy. Specifically, it reveals three state-sponsored spatial governmentality strategies (race-restrictive zoning, domestic space standardizing, and technological self-surveilling) used to transform perceived norm-breaking Roma immigrants (derogatorily referred to as “Gypsies”) into acculturating consumers to regional sedentary norms, as well as the accompanying Roma consumer resistance responses (community-protective insulating, domestic space rearranging, and behavioral boundary testing) used to partly contest the imposed consumer acculturation and preserve some of the minority’s nomadic culture. The article concludes with implications for research on consumer acculturation, consumer resistance, and spatial governmentality.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".