Producing Superstars for the Economic Mundial: The Mexican Predicament with Quality of Education
Bibliographic record
Abstract
Abstract. The question of how to build the capabilities to both initiate a resurgence of growth and facilitate Mexico’s transition into a broader set of growth enhancing industries and activities is pressing. In this regard it seems important to understand the quality of the skills of the labor force. Moreover, in increasingly knowledge based economies it is not just the skills of the typical worker than matter, but also the skills of the most highly skilled. While everyone is aware of the lagging performance of Mexico on internationally comparable examinations like the PISA, what has been less explored is the consequence of that for the absolute number of very highly skilled. We examine how many students Mexico produces per year above the “high international benchmark ” of the PISA in mathematics. While the calculations are somewhat crude and only indicative, our estimates are that Mexico produces only between 3,500 and 6,000 students per year above the high international benchmark (of a cohort of roughly 2 million). In spite of educational performance that is widely lamented within the USA, it produces a quarter of a million, Korea 125,000 and even India, who in general has much worse performance on average, produces over 100,000 high performance in math students per year. The issue is not about math per se, this is just an illustration and we feel similar findings would hold in other domains. The consequences of the dearth of globally competitive human capital are explored, with an emphasis on the rise of super star phenomena in labor markets (best documented in the USA). Finally, we explore the educational policies that one might consider to focus on the upper tail of performance, which are at odds with much of the “quality ” focus of typical educational policies which are often remedial and focused on the lower, not upper tail of performance. 1
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".