Bibliographic record
Abstract
Fuel prices, inflation and war have created the perfect storm for the green economy and fossil fuels alike. The presentation is special in its global focus on the perfect storm interconnections of components, much like a huge jigsaw puzzle for which all the pieces fit together, but in a complicated way.Renewables are expected to represent 90% of newly installed electrical generation capacity between 2022 and 2027, overtaking coal in the process. Electric vehicle (EV) sales are growing hastily in China and Europe. By contrast, North American targets are weak, leaving much room for automakers to continue to favour the more profitable gas-powered vehicles. Another constraint is the lack of availability of many EV models, with delivery wait times as long as 2 years or more.The U.S. Inflation Reduction Act (IRA) and the Bipartisan Infrastructure Law (BIL) combined, will catapult U.S. clean energy production plus close American EV and clean tech gaps with China and Europe. There was a mind-boggling momentum for green economy projects, existing, under construction and planned, prior to the IRA and BIL. The new legislative initiatives promise to stimulate massive investments green economy research, applications and R & D unparalleled elsewhere, with the possible exception of China.The IRA and BIL are complex and likely to give the U.S. a North American green advantage at the expense of Canada.Concurrently, with trillions in profits, the oil and gas sector is headed for gargantuan fossil fuel agenda. Though the sector was writing off tens of billions of dollars in 2020 and all signs point to peak oil and gas nearby, the short-term sector view has taken precedence. This oil and gas industry tunnel vision perspective is propelled by executive bonuses linked to production increases, 41% in the case of ExxonMobil and 20% for Shell. While the bonus criteria include transition positive elements, many of these elements may actually increase production and/or are greenwashing. Such is the case with characterizations of natural gas as a bridge fuel, howbeit shale gas methane emissions could render this fuel as bad as coal. Notwithstanding, greater production trumps all other considerations.This is what it is like in a transition, the path is bumpy with much tugging in opposite directions. Not unlike the long history of the struggle for women’s rights. The green transition shakedown is tramping ahead, but gamechangers are only noticed when tide is omnipresent.There are reasons that give hope for a green metamorphosis, but the foundation is shaky.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.069 | 0.031 |
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".