Localizing taste: using metaphors to understand loctural consumptionscapes
Bibliographic record
Abstract
The globalization of consumption or discourses of glocalization and hybridization dominate the extant literature on “consumptionscapes”. We introduce the “loctural consumptionscape” as an alternative that is centered on products of local-origin and draw upon conceptual metaphor theory to examine an Indian socio-cultural metaphor – traditional-sweets-consumption-as-shubh (auspicious). This metaphor involves the consumption of locally produced traditional Indian sweets. We find that various conceptual associations and relationships comprise the metaphor and these can be categorized into four dimensions – occasion, form and production, relationships – personal and social, and value. We further note that the taste of and for traditional Indian sweets is a key cultural sensibility that inhabits these dimensions. We employ such understanding to offer a view that is socio-culturally driven and which as a localized system of meaning distinguishes the loctural from other consumptionscapes in mass-ties of a horizontal rather than those of a hierarchical nature. The paper engages with the literature on the globalization of consumption by showing that cases of local consumption need not be examples of either anti-globalization or of hybridization, but a case of a search for a sense of cultural identity and authenticity rooted in indigenous products, consumed on appropriate occasions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".