Potential Impact of Environmental Activism: A Survey and a Scoping Review
Bibliographic record
Abstract
Environmental issues increasingly impact the well-being, the ability to have a good life, of people, especially members of marginalized groups. Dealing with environmental issues is a long-standing and increasing focus of activism. Youth are increasingly involved in environmental activism. One focus of environmental education is how to instill the role of being a change agent into students. Marginalized groups experience many problems in relation to environmental issues, and environmental activism impacts the lived experience of marginalized groups in diverse ways. A pre-study scoping review suggested a gap in academic inquiry around “the impact of environmental activism”. The aim of our study was to decrease this gap and to better understand the perceived impact of environmental activism. We used two approaches to achieve this aim. In the first step, we used a survey to ask undergraduate students about their views on the impact of environmental activism. Given the results of the survey and that students need access to information to be able to fulfill their roles as critical thinkers and change agents, we then performed a scoping review of abstracts from Scopus, Web of Science, and the 70 databases accessible through EBSCO-HOST to ascertain what topics and which marginalized groups are engaged with in the academic inquiry of environmental activism. We found that participants felt that environmental activism has an impact on all the social groups and entities we gave them as choices, although there were differences in how positively they viewed the different groups and entities being affected. The participants also indicated that many of the well-being indicators were impacted by environmental activism, although around 30% felt that they did not have information they needed to form an opinion. Finally, our participants felt that different social groups have different ability expectations. Our scoping review found that many of the groups and indicators that our participants felt were impacted by environmental activism were not covered in the abstracts we analyzed. Our findings suggest many gaps and the need for actions and opportunities in relation to the topic of the “impact of environmental activism”.
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 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 | 0.050 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.043 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".