Bibliographic record
Abstract
The concept of benchmarking can be defined in many ways. In general it is a method of increasing the performance and competitiveness of a company on the basis of comparing oneself with the best in a given field. The objective of this method is to gain information that will help to identify the strengths and weaknesses of a company with its competitors and which will serve as inspiration for improvement. Information is gained by constant observation and evaluation. At present, businesses, if they want to be competitive, should use this management tool to continually improve their performance across all company processes and activities. The professional literature presents different types of benchmarking; based on the organisation or field being compared, we can distinguish between performance, functional and process benchmarking. In practice, benchmarking uses a number of models; among the most well-known are the Xerox Corp., the American Productivity and Quality Center (APQC), the European Foundation for Quality Management (EFQM) or the Ontario Municipal Benchmarking Initiative (OMBI). Based on surveys conducted by Bain & Company on a long-term basis, benchmarking is one of the most popular and commonly used management tools, followed by strategic planning, customer relationship management, outsourcing, and vision and mission statements. The subject matter of this paper is the implementation of performance benchmarking in two Czech companies dealing with agricultural production. The aim of the benchmarking comparison is to identify the differences in the development of their financial situation, as well as to evaluate the specific indicators characteristic of agricultural production. In addition to the proportional indicators of the financial analysis, this paper includes a comparison of the subsidies drawn upon, the size of the land and selected cost items.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".