Bibliographic record
Abstract
Background: The research work on entrepreneurship, enterprise's policy and management, which started in 1992, successfully continued in the following years. Between 1992 and 2011, more than 400 academics and other researchers have participated in research work (MER research program) whose main orientation has been the creation of their own model of integral management.\n\n\n\n\tResults: In past years, academics (researchers and authors of published papers) from Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Byelorussia, Canada, the Czech Republic, Croatia, Estonia, France, Germany, Hungary, Italy, Poland, Romania, Russia, the Slovak Republic, Slovenia, Switzerland, Ukraine, and the US have cooperated in MER programs, coming from more than fifty institutions. Thus, scientific doctrines of different universities influenced the development of the MER model which is based on both horizontal and vertical integration of the enterprises' governance and management processes, instruments and institutions into a consistently operating unit.\n\n\n\n\tConclusions: The presented MER model is based on the multi-layer integration of governance and management with an enterprise and its environment, considering the fundamental desires for the enterprises' existence and, thus, their quantitative as well as qualitative changes. The process, instrumental, and institutional integrity of the governance and management is also the initial condition for the implementation of all other integration factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".