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Record W1569167050 · doi:10.22230/jem.2003v2n2a228

Improving knowledge exchange with technology tools

2003· article· en· W1569167050 on OpenAlexaff
Trina Innis

Bibliographic record

VenueJournal of Ecosystems and Management · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsKamloops Art Gallery
Fundersnot available
KeywordsGeneral partnershipKnowledge managementBusinessVariety (cybernetics)The InternetSustainabilityNatural resourceNatural resource managementKnowledge sharingComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Sustainability is a major driver of a number of international instruments including the Brundtland Report, Rio Declaration, and Agenda 21. All of these instruments emphasize the need to improve our management and exchange of knowledge.Sustainable management of natural resources requires the integration of information from many sources and disciplines, as well as close collaboration between geographically dispersed team members. Natural resource managers make decisions every day—to make sound decisions, they must have access to the best available information.Knowledge management systems address a variety of factors, including generation, organization, sharing, and application of knowledge. This paper demonstrates the value and applicability of various technology tools, including collaborative workspaces, Web conferencing, mailing lists, and Internet portals/knowledge repositories. The experiences of two organizations, FORREX–Forest Research Extension Partnership and FORCAST (Coalition for the Advancement of Science and Technology in the Forest Sector), are used to highlight the benefits of these tools within the natural resource sector and beyond.

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)0.000
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: none
Teacher disagreement score0.705
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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