Erdővagyon-újraelosztás és faipari nagyvállalatok megjelenése Háromszéken
Bibliographic record
Abstract
In the last quarter of the 19th century the forest industry has become the leading sector of the economy of Háromszék county. One must consider the huge forest properties that had changed their owners, the dimensions of the investment capital flowed, the development of the infrastructure in production and transport, and ultimately the amount of transported goods. The essentially economical process had antecedents regarding private, administrative and diplomatic law. Th e present study focuses on the interplay of the presented factors during several decades. Influences of private and community ownership changes, the attitude of the local administration towards the natural environment and the interests of the community, the border functions of the territory – marked from above by the reorientation that took place in the field of the state-theory at the level of the central government – are the factors that have crystallized in a medium-sized period, and that influenced the whole process. The study points out that in this economical environment the forest industry f tted successfully in a continental and cross-bordered structure.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".