Possibilities of Using Social Networks as Tools for Integration of Czech Rural Areas - Survey 2021
Bibliographic record
Abstract
This paper deals with the use of social networks in agricultural enterprises and focuses mainly on their role and share in increasing the competitiveness of agricultural enterprises in the market. Primary data were obtained from an extensive survey of the development of information and communication technologies in agricultural enterprises, which was conducted in the first quarter of 2021 throughout the Czech Republic (“Survey 2021”). The research was primarily focused on capturing current trends in the use of ICT with emphasis on selected key areas (broadband, social networks, communication tools, regional Internet portals, used hardware categories, used software, mobile communications, Internet of Things, data storage and security, social networks, etc.). This survey builds on previous extensive surveys conducted by the Department of Information Technologies, Faculty of Electrical Engineering, CULS in Prague in several phases since 1999, with the last stage being conducted in 2017. Some surveys were conducted in cooperation with the Ministry of Agriculture of Czech Republic.Compared to recent years, the survey includes new domains, such as the use of the Internet of Things in plant and animal production, data storage and security, the impact of the Covid-19 pandemic on the company's core operations, etc. The survey was prepared, conducted and administered by the Department of Information Technology, Faculty of Economics and Management, University of Life Sciences Prague.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".