MétaCan
Menu
Back to cohort
Record W2162936945 · doi:10.5558/tfc79865-5

Knowing a socially sustainable forest when you see one: Implications for results-based forestry

2003· article· en· W2162936945 on OpenAlexvenueno aff
Stephen R.J. Sheppard

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityStewardship (theology)BusinessForestryCommunity forestrySustainable forest managementEcoforestrySocial sustainabilityEnvironmental resource managementForest managementCertificationProcess (computing)Environmental planningPolitical scienceIntact forest landscapeForest ecologyGeographyComputer scienceEcologyEconomics

Abstract

fetched live from OpenAlex

The wider forestry community is struggling to define what the third leg of sustainability—social sustainability—actually means. While work is now underway to develop better social Criteria and Indicators for sustainable forest management in BC and elsewhere, it is already becoming clear that the social process of decision-making and management can be as important to society as the social outcomes. This has significant implications for a results-based system such as certification or a new Forestry Code in BC. This paper explores what a truly open and accountable planning process might look like. The achievement of social sustainability depends in part on society seeing tangible proof that forestry is ecologically sustainable and carefully designed. For many of the local and global publics, the forest landscape itself provides strong evidence of forest manager's performance. The concept of Visible Stewardship, the obvious expression of care and commitment to sustainable forestry, and emerging tools such as computer visualisation of future forests, may be vital to building trust in sustainable forestry. Key words: social sustainability, social criteria and indicators, public involvement, forest stewardship, visual quality, visualization, public perceptions

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.859

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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 designNot applicable
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

Citations36
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Forestry ChronicleSame topicForest Management and PolicyFrench-language works237,207