Bibliographic record
Abstract
George Ritzer has critiqued and built upon Marx's definition of the means of consumption to develop his own definition as “the settings or structures that enable us to consume all sorts of things” (2005: 6). These “new means of consumption” (a term used interchangeably with “cathedrals of consumption” by Ritzer) are more generally related to a wider field of goods and services and tied to production, distribution, advertising, marketing, sales, individual taste, style, and fashion. They are concerned not just with shopping but also relate to the consumer's relationship with entertainment and consumption‐oriented settings such as theme parks, casinos, and cruise lines, and other settings including athletic stadiums, universities, hospitals, and museums, the latter of which are surprisingly coming to resemble the more obvious new means of consumption. Examples include shopping centers such as West Edmonton Mall or the Mall of America, themed restaurants such as the Rainforest Café, and “brandscapes” such as Chicago's Nike Town. Such settings are considered important not just for their changing role as consumption settings, but also for the ways in which they are altering consumption more generally and the role many of them play as powerful American icons in the world (Ritzer & Ryan 2004).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".