Bibliographic record
Abstract
Business units meet many dynamic and unexpected changes occurring in their surrounding. They want to be competitive and that is why they must deal with uncertainty and risk. Digital revolution is present in all societies. Changes caused by usage of new technologies based on ICT technologies and Internet change the business. Nowadays enterprises must adjust to new determinants and to introduce digital technologies into their management processes, controlling systems. Companies now require fast and accurate information.Big Data technology brings to modern companies many technological opportunities. Big Data allows the creation of new business models, proper analysis of customer behavior, facilitates risk and financial management of the company, optimizes processes occurring inside the enterprise and increases the efficiency of IT systems operating in controlling of business unit.The aim of the paper is to present some theoretical issues connected with the use of Big Data technology in businesses, especially within systems of controlling (“Controlling” in the meaning used by German speaking scientists). Digital revolution with usage of social media in business activity or Big Data technology changed the controlling systems, because now controllers must deal with such huge datasets to improve their businesses. With usage of Big Data technology, despite some challenges occurred, modern enterprises can deal with success with such large volume of data (not only financial, but now also non-financial) in order to proper better forecasts and decisions. There are presented some issues connected with usage of Big Data technology in controlling systems of companies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".