RESEARCH INTO CHANGES TO SIGNIFICANCE OF CROATIA IN CANADIAN IMPORT AND EXPORT WITHIN SPECIFIC INDUSTRIES: COMPARISON BETWEEN YEAR 2016 AND YEARS 2011-2015
Bibliographic record
Abstract
This research and the associated paper represent the direct sequel of last year’s paper that marked the beginning of long-term research on significance of Croatia in Canadian import and exportwithin various industries by the same authors. While the focus of last year’s paper was on 5 years from 2011 to 2015, in this year’s paper those 5 years were being compared with the previous year 2016 that can be perceived as a kind of turning point in the relations of countries concerned, primarily through relations of EU (28 member countries, including Croatia) and Canada (10 provinces and 3 territories). With regard to research purpose of this paper, it can be explicitly defined as: to conduct unbiased, purposeful and multi-year research into changes to significance that Croatia had in Canadian import and export within specific industries. For that purpose, authors have decided to utilise exclusively websites created by Government of Canada that provide direct access to sufficient quantity of indorsed, relevant and official statistic data. Those were the main reasons why aforementioned websites were chosen as the central source of data in order to accomplish the research purpose of this paper. In relation to used research methods and approaches, in this paper authors have intentionally used only the most appropriate data from website by Government of Canada’s Office of the Chief Economist under Trade and Economic Statistics section as well as under Trade Investment and Economic Statistics subsection. Those datasets were utilized as the central source of numerical data for this wide-ranging research mostly because of their constancy, dependability and accessibility. Additionally, supplementary data by other carefully selected associations and organizations were used to further explicate import-export interrelations between Croatia and Canada. Conclusions that authors have drawn from the study of numerical data that included years 2011-2016 and from the detailed analyses and syntheses of the most relevant numerical data that link Croatia and Canada as well as their industries, actually represent the major research results of this paper. For these obvious reasons, major research results that were so clearly formulated can be used in a variety of professional purposes and in wide range of scientific researches. Finally, main implications of this paper were multiple logical deductions that can be made based on analyses and syntheses of numerical data as well as on proper comprehension of discussions within this paper. Those logical deductions should be perceived as stepping stones towards better import-export interrelations between Croatia and Canada that have the potential to improve, but only if previous experiences (including mistakes and omissions) will be properly used in future import-export endeavours that must take in consideration both previously mentioned countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".