THE CRUDE OIL PRICES DECLINE DURING 2014-2016 PERIOD. CAUSES, EFFECTS, PROSPECTS
Bibliographic record
Abstract
A phenomenon with a strong impact on international oil markets was the sharp decline of the maininternational benchmark prices for international trade (Brent and WTI), from $ 105.7 $/barrel in June 2014, to $ 36/barrel in December 2015. Compared with previous episodes of decline, from the last three decades, the recent decline of pricescan be described, by its magnitude, duration, and effects, as an unusual event. The main drivers responsible for the recentdecline in oil prices compared to previous episodes indicate a predominance of factors related to the supply side, with importantsimilarities with the 1985-1986 episode. Both episodes occured after periods of high oil prices and rapid expansion of non-OPEC oil production in Alaska, the North Sea, Mexico, US (oil shale, tar sands and Canadian biofuels). Also in bothperiods of the price crash, OPEC changed its strategic objectives, moving from a policy of supporting the oil price (by reducingthe offer) to a new one favouring the market share (by maximizing supply).There were some recessions caused by high oilprices: 1973-1975, 1980-1981 and 1990-1991, when the oil price declined because demand has collapsed. Now, whenprices go down, it is invoked, paradoxically, the same fear of recession, though conditions are different. If it occurs, it would bethe most unusual recession - the first ever caused by a decline in oil prices.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".