Bibliographic record
Abstract
Contemporary shopping malls in Egypt have created new public spaces for lifestyle and leisure, which complement the commercial logic of consumer behavior. Mega malls in Egypt are simultaneously merging shopping, leisure, and entertainment, creating an ambivalence. They are representations of the globalized economy, but also manifest a certain uniqueness through their typology, their mode of insertion in the urban fabric and the type of public spaces created in them. This paper traces four new typologies in the design of six mega shopping malls in Egypt, constructed since 2010, as they integrate new public gathering spaces for leisure, recreation, and entertainment. Data on the new malls in Egypt was collected from corporate websites and promotional brochures, Google Maps and Street View, TripAdvisor, social media websites, visitor comments and news articles. A key finding is the trend of integration of large outdoor recreational spaces such as courtyards and plazas in mall design, the inclusion of a water element for attraction as well as the transition in function from simply offering goods and services to one that offers experiences and events to encourage recurring visits to the mall. The transformation of the mall parallels changes in conceptualizing the city of the 20th century as a large marketplace, an emporium of consumption, to conceptualizing the city of the 21st century as a large theatre and a festive place.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".