Joakim Wising
Biographic Data
| ID | 6717233 |
|---|---|
| NAME | Joakim Wising |
| GIVEN NAMES | Joakim |
| FAMILY NAME | Wising |
| SIGNATURE | WISING J |
| AFFILIATIONS | Umeå University |
| ORCID | 0009-0000-1587-6898 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Precision or division? Stakeholder perceptions and tensions in the digital transition of Swedish forests
Precision forestry technologies are promoted as solutions for managing competing forest values and mitigating land-use conflicts, yet their social and political implications remain poorly understood. This study examines how Swedish forestry stakeholders perceive the potential and risks of digital tools such as AI, remote sensing, and predictive modeling in relation to forest-related conflicts. Drawing on a future-oriented participatory workshop a…
Bloc Politics and Parliamentary Speeches in the Swedish Riksdag
How do political blocs form, consolidate, and dissolve? And can we trace these dynamics through the language of parliamentary debate? We address these questions by analyzing speeches in the Swedish Riksdag, using supervised machine learning to examine changes in rhetorical alignment among parties. Adapting classification methods developed for two‐party systems to a multiparty context, we assess rhetorical alignment between and within Sweden's pol…
Forest owners’ perceptions of machine learning
Machine learning is becoming increasingly important in environmental decision-making, particularly in forestry. While forest-owner typologies help in understanding private forest management strategies, they often overlook owners' relationships with technology. This is crucial for ensuring that data-driven advancements in forestry benefit society. Using Swedish forestry policy as a case, we applied Q-methodology to explore forest owners' perceptio…
No prominent works on this page.
Forest owners’ perceptions of machine learning
Machine learning is becoming increasingly important in environmental decision-making, particularly in forestry. While forest-owner typologies help in understanding private forest management strategies, they often overlook owners' relationships with technology. This is crucial for ensuring that data-driven advancements in forestry benefit society. Using Swedish forestry policy as a case, we applied Q-methodology to explore forest owners' perceptio…
Precision or division? Stakeholder perceptions and tensions in the digital transition of Swedish forests
Precision forestry technologies are promoted as solutions for managing competing forest values and mitigating land-use conflicts, yet their social and political implications remain poorly understood. This study examines how Swedish forestry stakeholders perceive the potential and risks of digital tools such as AI, remote sensing, and predictive modeling in relation to forest-related conflicts. Drawing on a future-oriented participatory workshop a…
Bloc Politics and Parliamentary Speeches in the Swedish Riksdag
How do political blocs form, consolidate, and dissolve? And can we trace these dynamics through the language of parliamentary debate? We address these questions by analyzing speeches in the Swedish Riksdag, using supervised machine learning to examine changes in rhetorical alignment among parties. Adapting classification methods developed for two‐party systems to a multiparty context, we assess rhetorical alignment between and within Sweden's pol…
Forest Management and Policy (2 works) · Perception (2 works) · Business (1 works) · Cohesion (chemistry) (1 works) · Community forestry (1 works) · Computational and Text Analysis Methods (1 works) · Economics (1 works) · Education Practices and Evaluation (1 works) · Electoral Systems and Political Participation (1 works) · Environmental resource management (1 works)