An Explanatory Model for Organizational Resilience to Inflationary Processes and Uncertainty in the Era of Tariffs 2025
Bibliographic record
Abstract
The trade tariff war is producing crisis effects in the markets, based on the information and announcements of tariffs by the US to its trading partners Mexico and Canada, with the possibility of a reciprocal tariff war, the problem is that final prices could skyrocket for all the nations involved, even in the entire value chain. and changes in supply chains, thus increasing inflation. The effect, called the Plaza Agreement 2.0, makes it possible to change investments and resilient organizational strategies, although the final intention is not to raise prices and rates, but as a starting point of advantage in negotiations, in relationships of dependency. The objective of this article is to explain from a mathematical model the capacity of resilience that has caused this turbulence in the market. The methodology used is documentary, financial and organizational research, the result obtained from the model is an optimized resilience with an average equal to 0.5985.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".