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Record W7039332433

Making and using environmental information : an analysis of the development and use of two GIS tools for public environmental engagement

2004· dissertation· en· W7039332433 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2004
Typedissertation
Languageen
FieldEngineering
TopicPolymer Science and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityFocus groupGeographic information systemVisualizationFocus (optics)Public participation GISPublic engagementGeovisualization
DOInot available

Abstract

fetched live from OpenAlex

Following the admonition to "think globally, act locally" has proven difficult. Discussions of sustainability often remain at a local level without addressing global sustainability and yet the need for local engagement is well demonstrated. Using the case of Montreal's West Island, two environmental information tools were created and then evaluated on their ease of development, ability to be publicly engaging and ability to link the local and global scales. The first tool was a GIS visualization of sub-municipal ecological footprints and the second was a more conventional GIS 'atlas'. Focus groups were used to test the two tools. The atlas tool was considerably easier to create, and both tools succeeded in engaging participants. Focus group analysis does suggest however, that while local land-use based maps remain advantageous for exploring specific local and structural issues, local ecological footprints are better able to facilitate local-global linkages.

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)0.021
metaresearch head score (Gemma)0.068
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.068
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.010
Science and technology studies0.0030.005
Scholarly communication0.0090.008
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.258
Teacher spread0.205 · 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
Published2004
Admission routes1
Has abstractyes

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