Latin American lockdowns speed Mercado Libre's growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Significance Latin America’s leading e-commerce platform has emerged as one of the winners in the current regional business environment, marked by the COVID-19 pandemic. While retailers across the region have struggled to cope with lockdowns, Mercado Libre saw its user numbers increase by 45.2% year-on-year in the second quarter, to 51.5 million. Mercado Libre has been able to adapt its business model to Latin America’s unique market features, which include excessive regulation, chronic infrastructure problems and poor enforcement of the rule of law. Impacts Mercado Libre’s expansion prospects in Latin America may prove strongest outside its home market, Argentina. The shift towards online retail in the region will continue as fintech expands among people not included in traditional financial systems. Regulatory difficulties and trade union pushback will continue to raise challenges in some markets.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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 it