MétaCan
Menu
Back to cohort
Record W4394465812 · doi:10.6084/m9.figshare.11373969

Understanding perceptions of the state of the environment in relation to ecological measures: Intergroup differences and the influences of an interpretive program

2021· dataset· en· W4394465812 on OpenAlexaboutno aff
Angela Mallette

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)PerceptionEcologyState (computer science)PsychologyGeographyComputer scienceBiologyData mining

Abstract

fetched live from OpenAlex

This research project examined how the state of the environment is understood through an ecological and social perspective. Emphasis is placed on ecological measures as well as perceptions, with specific attention to intergroup differences and the influences of an interpretive program. Two studies were conducted at the Niagara Glen Nature Reserve, a protected area in the Niagara Region of Canada. The first study consisted of an ecological assessment and a survey administered to experts and visitors. The second study involved administering the survey to individuals receiving two different educational interventions, thereby exploring the influence of an environmental interpretive program on how people perceive the environment. Raw data, summary data, and procedure details included here.

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)0.004
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.207
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0480.018

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 head0.094
GPT teacher head0.333
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicEnvironmental Justice and Health DisparitiesFrench-language works237,207