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Record W7047627078

Identification and application of the components of meaningful public participation in forest management

2005· dissertation· en· W7047627078 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2005
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsPublic participationIdentification (biology)Community participationForest managementSocial engagementData collection
DOInot available

Abstract

fetched live from OpenAlex

Public participation is a central principal of sustainable forest riranagement and is increasingly seen as an important method for facilitating fair and effective decisionmaking in forest management.Public participation is rapidly moving from a policy goal to a legal requirement in forest nuriagement in Canada.However, achieving meaningful participation continues to be a challenge.There is a growing body of research that is attempting to uncover and define what elements make public participation processes effective.This study builds upon this research by examining what makes a particþation process meaningful and investigating the potential for implementing more meaningful public participation in forest management.To achieve this, the specific objectives of this study are: 1) to establish the key components of meaningful public participation;2) to investigate current appro.aches to public participation in forest management planning; 3) to corsider levels of satisfaction with current participatory approaches within Manitoba's Mountain Forest Region by examining current practice in light of the components of meaningful public participation; and 4) to develop recommendations for public participation in forest management.A qualitative research approach was used to address the goals of the research including, structured standardized expert interviews, semi-structured participant interviews, and a review of the relevant literature.Structured standardized interviews were conducted with academics, practitioners, and professionals involved in the public participation field-The rezults of these interviews were used to develop the key i Abstract components of meaningful public participation These components were vetted and built upon during the second phase of interviews involving participants from four public participation initiatives in Manitoba's Mountain Forest Region.The results established a definition of meaningful public participation and several components of meaningful public participation.The components of meaningful public particþation identified by this research include, fair notice and time, integrity and accountability, fair and open dialogue, multiple and appropriate methods, learning and informed participation, adequate anl accessible information, participant motivatior¡ inclusiveness and adequate representation, and influence.The components of meaningftl public particþation outlined in this study provide insight into how to run a more meaningful public participation process and show promise for use as a straightforward guideline for developing and implementing public participation processes that are more meaningful.First and foremost, I would like to thank all of the interviewees for sharing their knowledge and experiences with me.I would also like to extend my gratitude to Louisiana Pacific, Manitoba Conservation, and Parks Canada for allowing me to study their participation processes.Secondly, I extend thanks to the members of my academic committee, all of whom have been a valuable source of guidance, support, and advice throughout the research process.Dr. A. John Sinclair

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 imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.005
Science and technology studies0.0060.011
Scholarly communication0.0100.006
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2005
Admission routes2
Has abstractyes

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