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Record W7062298323

A Student In The Forest: An explorative application of the framework of transition management for sustainability transitions in forest governance in Kosovo

2023· article· en· W7062298323 on OpenAlexfundno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersMinistry of EnvironmentEuropean CommissionUnited States Agency for International DevelopmentManitoba Agriculture, Food and Rural DevelopmentStyrelsen för Internationellt UtvecklingssamarbeteUnited Nations Development Programme
KeywordsCorporate governanceTransition management (governance)SustainabilityWork (physics)Transition (genetics)Language changeForest managementSustainable developmentNetwork governance
DOInot available

Abstract

fetched live from OpenAlex

Contemporary and future societies are facing a myriad of challenges, some of which are throwing into question the future viability of humanity on Planet Earth. These challenges are complex and systemic, and to solve them we must transition to more sustainable ways. Increasingly, researchers have a role to play in not only researching transitions but also facilitating transition through research design which postulates closer engagement in practical contexts and empowering actors of governance to identify pathways for transition. Transition management offers a framework for those interested in bringing about such transitionary potential through research, and potentially offers a tool for students interested in using their university work as a means to bring about transition. This study applies transition management framework using Kosovo’s forest governance as a case study asking the question: How can Kosovo transition towards more sustainable forest governance? The paper finds that forest governance in Kosovo is highly chaotic and inefficient. Issues of lack of capacity, competencies, expertise as well as knowledge are coupled with legal framework which does not allow for local ecological contexts nor local needs to produce a regime of governance subject to environmental degradation, corruption and contestation. Moreover, the absence of capacity on both central and local levels means that the regime configuration is very weak and malleable, and niches have potential to bring about transitions in regime configurations. For Kosovo, moreover, it finds that operative activities are not only possible but desirable, and that actors engaged in forest governance have good conditions and a receptive society for transition. None of the actors which participated in the study were happy with the situation and the need for change is well understood. The challenge for Kosovo is thus identified as how best to facilitate participation for transition, how best to balance centralised vs. decentralised governance, as well as how best to facilitate learning through both raising awareness and listening. These issues are found to be best pursued collectively with wide participation. It identifies three suggestions for transitions which are aimed at bolstering and streamlining existing niche innovations in Kosovo: i) participatory forest governance plans, ii) a forest community centre as well as iii) citizen science initiatives. Lastly, it reflects on the process of involved, participatory and complexity-oriented research to address complex issues, as well as the merit of transition management. It finds that whilst epistemologically diverse and more action-oriented research is important and productive for students of Sustainable Development, even necessary, the transition management framework is difficult to implement, cumbersome and possibly not possible for students. Whilst strategic, tactical and reflexive activities are possible, operative activities are more difficult and exposes the weak standing of students within the politics of environmental governance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.294
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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