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Record W4395693594 · doi:10.15402/esj.v10i1.70861

Silence: A Novel Co-Produced Experience To Build Community Awareness Of Biodiversity Loss

2024· article· en· W4395693594 on OpenAlexvenueno aff
Kristen Bellisario, Christie Shee

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSilenceBiodiversityPsychologyBusinessEnvironmental resource managementEnvironmental scienceAestheticsEcologyArtBiology

Abstract

fetched live from OpenAlex

The current sounds of our world are under threat of disappearing. Undergraduate students and interdisciplinary university teams are at the forefront of generating collaborative research opportunities to create community awareness of biodiversity loss and conservation practices. Recent conservation research has focused on how local communities can begin to reverse the trends of biodiversity loss by using private residences and urban spaces. The inclusion of native plants in backyard gardens is an accessible way to promote ecological restoration. In this co-produced instructional exhibit, “Earth Day Celebration: Silence,” we introduce a novel experiential event that connects instructional design with community collaboration. The event was designed to explore the ways in which society can become engaged in the preservation and protection of biodiversity and our sonic world.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0040.005
Scholarly communication0.0040.003
Open science0.0030.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0120.003

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 head0.124
GPT teacher head0.397
Teacher spread0.272 · 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
Published2024
Admission routes1
Has abstractyes

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