Bibliographic record
Abstract
An integral component of successful entrepreneurial activity is the establishment of strong relations with partners, the form and type of which varies depending on the sphere of activity and its scope. Considering that a prerequisite for a business partnership is constant communication, the style, and quality of which mainly determines the success of doing business, the development of business communication skills becomes an object of continuous research by both scientists and enterprise managers. The main goal of this study was to examine the role and the importance of business partnerships in Google Trends. Another goal of the study was to look at how often the term business partnership appears in Google books, with the help of Google Books Ngram Viewer, as well as in a database of Science Direct, with the help of Science Direct’s search function. The top year of the interest frequency of business partnership was 2004 in the Google Trends worldwide in business and industry categories. The bottom year of interest frequency was 2006. The geographic analysis revealed that most people searched in Botswana, in Jamaica, in Zimbabwe, in Ghana, and Uganda for the term business partnership. Interestingly, these countries are all located on the African continent, except Jamaica. Most people searched in Accra, in Nairobi, in Manila, in Quezon City, and Cebu for the term business partnership. The results of the bibliometric analysis of the relationship of business partnerships with other categories made it possible to conclude that people (mostly from the Philippines, Ethiopia, the USA, Canada, and Kenya) inquired about the joint venture and limited partnership. The conducted study revealed that from 1950 to 1970, the frequency of appearance of business partnership in the Google Books` database has been decreasing continuously. From 1972 the frequency has been growing gradually, then from 2006, the frequency has been decreasing gradually. The use of the term “business partnership” shows in the Science Direct a second-degree polynomial growing trend. Summarizing all these results, we can conclude that while people in developing countries in Africa and Jamaica were most sought for expressing business partnerships, people in developed countries were more interested in types of business partnerships. Keywords: Business partnership, Google Trends, Google Books Ngram Viewer, Science Direct, Time Series Analysis.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".