TUC City Profile: Buenos Aires, Argentina
Bibliographic record
Abstract
While it is the jurisdiction with the highest per capita gross domestic product (GDP) in Argentina, there are historically marked differences in socioeconomic levels and socio-environmental conditions between the north and the south of the city of Buenos Aires. The effects of climate change are intertwined with those of economic globalization, a process of “double exposure” that disproportionately affects particular social groups and areas in the city. Slums and informal settlements in Buenos Aires are growing in size. Most face significant resource and infrastructure deficits, as well as high flood risk, and are thus highly vulnerable to the effects of a changing climate. Tackling climate change in Buenos Aires requires integrated adaptation and mitigation approaches that put the most vulnerable at the centre. The ongoing socio-urban integration processes in informal settlements represent opportunities to rethink and territorialize climate action from an integrated habitat perspective. Since the early 2000s, Buenos Aires has built a robust track record of climate policy, including a climate change law and three Climate Action Plans (PACs). The city has also long been a hotbed for social movements, with a recent resurgence of “the right to the city,” defined as the right of urban dwellers to build, decide and create the city. This provides fertile ground for climate justice narratives and transformative climate action to take root. More than half of the city’s GHG emissions come from the consumption of grid electricity and fuels in buildings, mostly natural gas. Multilevel and intersectoral articulation of public policies are key to advance the climate agenda at the city level, particularly in light of limited urban authority over the electricity sector. Sustainability transformations in Buenos Aires could also be enabled by strengthening the existing capacity development efforts of particular local actors to raise climate awareness; connecting and amplifying emerging community-led initiatives that showcase transformative climate action; and clarifying financial flows as a way to stimulate climate financing.
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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 | 0.001 | 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 | 0.002 | 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) | 0.076 | 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".