The evolution of small-scale forestry in Norway and its changing impacts on ecosystem health and the economic viability and societal well-being. A focus on the living forests process.
Bibliographic record
Abstract
Norway is a country of small-scale forestry, with most of the forest ownership attached to small farm-foresters. The forest owners are organised through a series of Forest Owners Associations, who play a key role in working with the government on forestry and related rural development policy and legislation. Norwegian forestry has traditionally played an important role in the economy and cultural context of the country. The law of virtual free access (Everyman’s Right) to private land for recreation and activities such as walking, skiing, berry or mushroom gathering for private use, has supported the frequent use of the forests by many Norwegians. Forestry has always remained an important issue in terms of its environmental, economic and social impacts. Some of the factors that have influenced the evolution of small-scale forestry in Norway (Mitchell-Banks 2005) include, but are not limited to: • Global forestry markets in which new cheaper suppliers of forestry products have entered the market and have captured previous markets from Norwegian forestry companies – examples are countries such as from the Baltic States, Poland, etc. • Traditional forestry countries such as Finland and Sweden who have increased their forestry output and become more competitive • Rural-urban migration and the increasing absentee forest owners in Norway • The change over the last four decades in the Norwegian economy driven by the discovery of vast exploitable oil and gas reserves off the coast and the economic implications of this dominant economic sector • The impediment to forestry property sale and transfer through Allodial Law which significantly impedes the creation of larger small-scale forests. The Living Forests Project was the first time forestry had been addressed in Norway on a participatory basis with broad representation of stakeholders. This process started in the 1990s and was equally funded by the Norwegian Government and the forest sector and Forest Owners Associations and involved representation from the Government, Forest Sector, Forest Owners Associations, and Non-Governmental Groups representing recreational, environmental, labour, women and other interested stakeholders. This paper will take a particular look at the role that the Living Forests Project has played in Norwegian Forestry and the influences it has had on SmallScale Forestry’s effects on the Environment, and Economy and Social-wellbeing of the rural communities of Norway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".