The world turned upside down: the energy of geopolitics—today and tomorrow
Bibliographic record
Abstract
The energy of geopolitics will be profoundly different in the new decade. Tectonic shifts in the energy landscape are occurring as we move headlong into this new decade. What we are looking at is nothing less than a world with a new geography, a new road map, and new technology enablers—and with this comes shifting supply and demand dynamics. The energy of geopolitics will be driven by a shift in geography—with the Saudi-Caspian-Siberian-Canadian corridor of supply meeting the new epicenters of demand in China, India and Asia. Meanwhile, a powerful new energy enabler—information technology and smart technology—will take on a critical role transforming the demand curve, while also ushering in a new mantra: clean energy, not green energy—in other words, energy that is both supply-abundant and clean, meaning that it is carbon neutral or carbon reducing. In the next decade, consumption will be the primary driver of the new energy roadmap, becoming a conscious act and an act of conscience—and new energy enabling technologies will pave the way. Technology will shift the global energy focus to a wide variety of clean-energy technologies: renewables such as wind and solar, carbon-scrubbed oil, natural gas and coal, as well as nuclear and efficiency systems. Consider the current proliferation of shale gas in the United States, which demonstrates how technology can help us tap into energy that is abundant and relatively clean. The same technology may be the enabler for a known, but as yet untapped source of oil—shale oil. Importantly, technology is driving both supply and demand dynamics, but with a difference: new technologies are locally embedded yet globally connected. This all adds up to a sea change in how energy transforms our futures. Whether it is foreign policy, the environment or the public’s pocketbooks, energy is entering a world turned upside down. Energy is the Great Game 2.0—a shift from a resource-driven world to one driven by technology.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".