Governance of forests and governance of forest information: Interlinkages in the age of open and digital data
Bibliographic record
Abstract
Policy processes to open digital forest data and information are driven by expectations of increased effectiveness and efficiency of forest management, greater transparency of forest decision making, development of new innovations, and transition to bioeconomy. We investigate how interlinkages between the governance of forest information and governance of forests are being reshaped in the formation of new institutions for open data and information in the case of Finland, where the Forest Information Act was revised in 2016–2018. A qualitative content analysis of public statements related to the legal reform was conducted to understand the perceived benefits and risks associated with more open forest data and information by different actors, and how those perceptions shape their views on appropriate governance of forest information. The analysis reveals conflicts between right to information and right to privacy; concerns about data format, access and usability; as well as the interests of actors with entrenched positions in Finnish forest governance. The debate on opening forest information reflects tensions related to a transition towards greater openness and diversity of values in the forest sector. We envision further research on the relationship between the governance of forests and governance of forest information to support informed decision making during the current open data boom.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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 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".