Dynamics of employment in sectors of intensive use of knowledge in the Megalopolis of the Valley of Mexico, 2014-2018
Bibliographic record
Abstract
Qualified human capital is key to the progress of cities. The objective of this work is to answer the following questions: how much, where, in which sectors, when, and why did employment change in knowledge-intensive sectors (KIS) in the Megalopolitan Region of the Valley of Mexico, its main cities, and in its municipalities? This Megalopolis is the main engine of national development. It is necessary to understand the dynamics of the Megalopolitan KIS employment, to take advantage of its positive effects, and prevent negative ones. The analysis period is 2014-2018: it covers the last section of the Free Trade Agreement between Mexico, the United States, and Canada, before the modifications negotiated in 2019, and almost a whole period of the federal government. The method is Spatial-Shift Share Analysis (SSS). It was possible to identify ascending territories, and others in dramatic decline. We translate the SSS concepts into causes of employment change, measure the changes at three spatial scales (megalopolitan, metropolitan and micro-spaces), and linked the changes to specific companies and products, in particular territories and sectors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".