MétaCan
Menu
Back to cohort
Record W7132963793

"Green" Doesn't Always Make Good Impressions: Evaluations of Different Types of Environmentalists

2012· dissertation· en· W7132963793 on OpenAlexaff
Nadia Yasmine Bashir

Bibliographic record

VenueTSpace · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsMainstreamPower (physics)Social perceptionAffect (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

In the present research, I examined individuals' evaluative responses toward traditional representations of environmentalists (e.g., tree-huggers and radical activists) as well as less typical but more mainstream environmentalists. Undergraduate students read about one of three types of environmentalists (i.e., radical activist, tree-hugger, or mainstream environmentalist). Participants then rated the extent to which they liked the individual they read about. Results revealed that participants evaluated the tree-hugger and radical activist less favourably than a typical student. In contrast, participants responded as favourably toward the mainstream environmentalist as they did toward a typical student. These findings indicate that individuals have distinct impressions of different types of environmentalists: Whereas mainstream environmentalists may receive favourable evaluations from individuals, stereotypical environmentalists may elicit negative reactions and even alienate members of the public.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.355
GPT teacher head0.525
Teacher spread0.171 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicClimate Change Communication and PerceptionFrench-language works237,207