INTERDEPENDENCE OF TRANSPARENCY AND BUSINESS SUCCESS OF THE COMPANY
Bibliographic record
Abstract
Timely disclosure of information and data brings to enterprises many benefits in the business, which is reflected in attracting domestic and foreign investors, which is a particular aspect of corporate governance. However, countries in transition are faced with low level of transparency, due to unclear legal regulations, as well as the disorganization of the market. The specific goal of this paper is to analyze the role and importance of transparency from the perspective of corporate governance on the business success of the companies. This research was conducted in the last quarter of 2013 on a representative sample of companies from city of Doboj, which are quoted on the Banja Luka Stock Exchange. Analysis includes the review of material significant, useful information broadcasted on the websites of companies, as well as any other information which is published and contributes to improve the corporate governance of the observed associations. Therefore, the aim of this paper is to highlight the best solutions in order to increase the transparency of the company. Pragmatic contribution of this paper will have domestic and foreign investors, managers and other interested public in the area of Doboj region, but also Republic of Srpska
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 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".