Penurunan Perdagangan Bilateral Indonesia-Kanada Periode 2015 – 2016
Bibliographic record
Abstract
Indonesia and Canada like the relations of any sovereign states in the world, conduct cooperation in various fields including trade. The bilateral trade that was initially adequate in terms of volume and benefiting both sides in the early 2000s, took a drop since 2011. The negative trend reflected on the impediment of trade between the two nations with a rate of – 4,72 % in the 2011-2015 period. Thus efforts had been conducted with the aim to propel the two nations’ bilateral trade, including the will expressed by the two nations’ head of states during the G-20 Summit held in Turkey on 2015. The problem arises as the bilateral nations’ trade plunged deeper shown by a decrease of – 14,52 % in the year after or during the 2015-2016 period. Hence the writer conducted a research in order to determine the factors affecting Indonesia-Canada’s bilateral trade to shrink even further in the 2015-2016 period, also to find empiric proofs of any further action taken as part of the will to increase trade by the two heads of nation. This research was carried out by utilizing the explanative-qualitative method, with the range covering a period of 2014 – 2017 or a year before and after such phenomenon occurred. Furthermore from the research, it could be inferred that various factors such as non-tariff barriers (NTB), tariff barriers (TB) being used by Canada against Indonesia’s main prestigious commodities, as well the statist-nationalist trade policy taken by President Joko Widodo’s government adding up to holding back and contributing to a more significant impediment of Indonesia-Canada’s bilateral trade.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".