Understanding the Effects of Corporate Environmental Communication on Jobseekers
Bibliographic record
Abstract
ABSTRACT Misleading corporate environmental claims, such as greenwashing, continue to increase, and the question of how jobseekers are affected by organizational greenwashing remains understudied. The present research conducts an experimental study on real jobseekers through Amazon Cloud Research, across two phases of online survey research. Participants viewed a fictitious corporation's website, which randomly exposed one of either a misleading environmental claim (greenwashing), an environmental claim substantiated by a neutral third party, or no environmental claim (i.e., control). Findings demonstrate that participants exposed to greenwashing reported higher levels of organizational cynicism when compared to control and substantiated claim conditions. Further, when greenwashing and control conditions were contrasted, the need for cognition moderated the link between claim and organizational cynicism, which predicts jobseekers' organization evaluations and environmental engagement. When greenwashing and substantiated conditions were contrasted, organizational cynicism mediates the effect of claim on organization evaluations and environmental engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".