Bibliographic record
Abstract
In an essay entitled “Disjuncture and Difference in the Global Cultural Economy,” Arjun Appadurai proposes a framework for understanding the process of global cultural flow, particularly in the field of economics. The term “culture,” in this sense, is summarily defined as “behaviour and beliefs that are learned and shared: learned so that it is not ‘instinctual,’ shared so that it is not individual” (Pieterse, 1390). Essentially, Appadurai’s framework is segmented into five distinct categories, or “scapes,” including: (a) ethnoscapes, (b) mediascapes, (c) technoscapes, (d) financescapes, and (e) ideoscapes (589). As products or attributes from various communities are introduced into new societies, they are inclined to go through a process of indigenization, often resulting in configurations of cultural hybridity and exchange. This process is evident in global music and media business networks, informing artists and professionals alike on emerging models, markets, and trends within the seemingly limitless boundaries of the global stage. The global cultural flow of music and media incites measurable consequences across all five of Appadurai’s scapes; in turn, this activity informs the character and development of the geographical sites facilitating such exchange. One such relationship exists between Lisbon, Portugal and Toronto, Canada, or more specifically, Lisbon and the Toronto neighborhood known as Little Portugal. Stemming from a complex history of colonialism and immigration, Lisbon and Toronto have developed as symbiotic nodes among the larger structure of global music and media networks. By examining each of Appadurai’s scapes within the context of Lisbon and Toronto’s transnational relationship, one can better understand the effects of global music exchange and the development of the music city itself.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".