Bibliographic record
Abstract
Introduction In 2002, Neil Smith published a characteristically provocative article in which he claimed that gentrification, which had ‘initially emerged as a sporadic, quaint, and local anomaly in the housing markets’ of cities in the advanced capitalist world, had become a ‘thoroughly generalized’ ‘urban strategy’ (Smith, 2002, p 427). The incidence of this strategy was, he argued, now ‘global’ and a ‘consummate expression of neoliberal urbanism’ (Smith, 2002, p 446). Smith acknowledged the rescaling of urban relations in a more global economy, but he took aim at writers who regarded the process as driven by finance and consumption, arguing that globalisation was based on production, albeit now in broader and more tightly connected circuits of capital and culture. Smith, however, paid little explicit attention to the relation between culture and gentrification, and he illustrated his claims of a global urban political economy from the experience of New York City. In this chapter, I want to consider Smith's broad arguments in and from the perspective of Mexico. Specifically, the chapter revisits the city of Puebla, the site of a set of articles that had their point of departure in whether the debates surrounding the ‘ideal type’ of gentrification as conceptualised in the North could ‘travel’ and offer analytical traction in the South (Jones and Varley, 1994, 1999). I outline the arguments of this research later in the chapter. However, for now, it is useful to note that we perceived gentrification in Puebla at that time to be very different from what we understood to have happened in New York City, London or Vancouver. What was unclear to us then, however, and what I want to explore in this chapter as I take the experience of Puebla forward, is whether the particular form of gentrification in the 1990s represented a ‘variation’ on a norm or a different process. In particular, this chapter considers how gentrification is affected by the changing processes of urban change in neoliberal times. Following Smith, the link between gentrification and neoliberalism requires some specificity. I am cautious about making a claim that gentrification is intrinsically linked with neoliberalism or that the latest ‘phase’ of one is coterminus with the most recent iteration of the other.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".