Can Conflict Be Planned Away? A Critical Assessment of Participatory Land Use Planning in Swedish Forest Governance
Bibliographic record
Abstract
Abstract A widespread governance response to land use conflict is to seek improved communication through the employment of dialogue-based instruments. In this paper, we interrogate the guiding presupposition that conflict can be planned away through a case study on the Reindeer Husbandry Plan ( Renbruksplan ), a tool used to address land use conflicts between industrial forestry and Indigenous Sámi reindeer herding. Drawing on critical policy analysis and environmental justice frameworks, we analyze the problematizations, silences, and effects emerging from the tool’s use in forestry planning and land use decisions. Our findings reveal that, operating in its current institutional and legal context, the tool offers limited improvements in procedural justice, exacerbates unequal distribution of burdens and benefits in terms of who gets to use forest resources, privileging a forestry-centered representation of the land use conflict. We therefore conclude that, in absence of institutional reform, the tool is likely to perpetuate conflicts and continue to reproduce the injustices embedded in Swedish forest and land use governance. Responsible Editor : Lena Gross, Norwegian Institute for Cultural Heritage Research, Norway
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.
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 | 0.055 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".