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Record W1668335797 · doi:10.25959/23209157

Improving the theory and practice of community engagement in Australian forest management

2011· dissertation· en· W1668335797 on OpenAlexfundaboutno aff
MA Dare

Bibliographic record

VenueeCite Digital Repository (University of Tasmania) · 2011
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersInternational Council for Canadian StudiesUniversity of Tasmania
KeywordsForest managementCertified woodCertificationEnvironmental resource managementLegislationCommunity forestryBest practiceCommunity engagementBusinessPublic relationsPolitical scienceEnvironmental planningGeographyForestry

Abstract

fetched live from OpenAlex

Community engagement (CE) is an integral component of modern forest management. Providing opportunities for dialogue between forest managers and those community members impacted by or interested in forestry operations, CE enables the inclusion of diverse public values and priorities in decision-making. This thesis examines current CE practice within Australian commercial plantation forest management. In answering the research question, How can the theory and practice of community engagement in Australian plantation forest management be improved?‚ÄövÑvp, several important observations regarding the effectiveness of current CE practice are made. Some 65 key informant interviews were conducted with a range of forest managers and community members. Research was primarily undertaken in Tasmania and Western Australia, although further interviews were conducted in New Brunswick (Canada) to help identify similarities in forest management practices and identify key learnings. The interviews highlighted the diversity of CE approaches used within operational forest management decision-making. Major findings include that CE is a well-established norm within Australian commercial forest management, with techniques ranging from basic one-way informing techniques to collaborative management committees. However, while CE is well accepted and adopted by forest managers, their approaches to, and the extent of CE utilisation are often limited. Operating within a highly regulated environment, forest managers frequently apply narrow forms of CE to seek compliance with various regulatory mechanisms (e.g. legislation, codes of practice, forest certification). Such practices are rarely informed by the underlying theoretical and social considerations of CE, including inclusivity, representation, power, and trust. Requirements for CE within current regulatory frameworks do little to improve CE practices, nevertheless there is evidence that the reporting process associated with forest management governance (in particular forest certification) is helping to improve CE practice and understanding within the industry. Continual documentation and review of CE processes is promoting a more reflexive approach to forest management, encouraging forest industry CE practitioners to think back on CE activities and learn through experience. Effective CE is often thought to be vital in the achievement of a 'social license to operate'. This research, however, indicates that operational forms of CE have a limited influence on achieving a social license to operate. This is due to the often significant influence of other factors, including the prevailing governance frameworks, the media, and the broader socio-political context of forestry. While operational CE can help to ensure a localised social license to operate is obtained, more effort in understanding, and if necessary overcoming, these limiting factors is required in order to achieve a broader social license to operate. This thesis is presented as a series of papers which collectively provide a broad picture of current CE practice within commercial Australian plantation forest management. Grounded in the commercial reality of modern forest management, the thesis aims to present a realistic picture of current CE practices and provide a rational and feasible guide to improved CE practices.

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 score0.574
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.219
Teacher spread0.200 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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