Deformation of entrepreneurship in the forestry sector of the economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The highest level of shadow activity is in the service sector and trade, this is primarily due to the specifics of the activity. However, a relatively small percentage of shadow income in the forestry sector indicates that damage is caused not only to the social and economic subsystems of the national economy, but also to the ecological, natural, and recreational ones, which indicates the high urgency of this problem. Our country, having the largest forest reserves, receives in the form of export earnings from the sale of forest products about 5 billion dollars, while Finland - 10 billion, Canada - more than 25 billion, the United States - more than 15 billion, Sweden - 12 billion dollars. Speaking about the development of entrepreneurship in the forestry sector of the economy, it should be noted that its formation took place in an extremely short time. Reforming forestry legislation has created the foundation for starting entrepreneurial activity in all forms - public, private, mixed. In a little over three years, the problem of leasing a significant part of the forest fund by entrepreneurial structures has been solved. The best and most economically available forest resources were in the hands of entrepreneurs. Of course, the transformation of forestry legislation was carried out not only in the Russian Federation and took place in different ways. The consequence of these transformations were changes in the basic factors of forestry production: economic; ecological; social. It should be noted that along with the changes in forestry legislation in a short period of time, the transformations that took place in the forest sector have caused multiple deformations, including in the development of entrepreneurship.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it