Bibliographic record
Abstract
Canadian represents a good case study to examine the effect of oil price, because most of its earning dependence on exporting crude oil. Canadian is one of the major oil exporting countries. Generally, the national income depends on crude oil. Oil revenue in Canadian covers 90 percent of Canadian government’s budget and also Canadian economy could be effect by would economic during economic problems. Thus, increasing oil crude oil price can affect on economic growth in Canadian. So it is crucial to use other resource instead of oil revenue as a new strategy to gain national revenue. The main objective of this study is to examine the effects of oil price and oil production value on economic growth. Annual growth rate, compound growth rate and correlation coefficient can be used to estimate of the data. The data is annual data which were converting a period of 21 years from 1995-2017. As a result, Economic growth is one of the most important sources of economic transformation because it reflects the community's ability to increase productive capacity and optimal investment and also sustainability requirement includes a diversified economy on the face of shocks, dynamically adopts technology and head accumulation human money, competitively can gain relative advantages compared to the other. Thus, it operates within stable, stable economic policies and economic development and there was positively statistically significance between oil price and GDP, oil production value and GDP.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".