Coverage of Disabled People in Environmental-Education-Focused Academic Literature
Bibliographic record
Abstract
Environmental education (EE) is a lifelong process to acquire knowledge and skills that can influence pro-environmental behavior, environmental activism, and disaster-risk management. Disabled people are impacted by environmental issues, environmental activism, and how EE is taught. Disabled people can be learners within EE but can contribute to EE in many other roles. Given the importance of EE and its potential impact on disabled people—and given that equity, diversity, and inclusion is an ever-increasing policy framework in relation to environment-focused disciplines and programs in academia and other workplaces, which also covers disabled people—we performed a scoping review of academic literature using Scopus and EBSCO-HOST (70 databases) as sources, to investigate how and to what extent disabled people are engaged with EE academic literature. Of the initial 73 sources found, only 27 contained relevant content whereby the content engaged mostly with disabled people as EE learners but rarely with other possible roles. They rarely discussed the EE impact on disabled people, did not engage with EE teaching about disabled people being impacted by environmental issues and discourses, and did not connect EE to environment-related action by disabled people. Results suggest the need for a more differentiated engagement with disabled people in the EE literature.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.062 | 0.088 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".