"Empowered to Change the World for the Better": Gender, Citizenship, and Justice in Three Environmental Education Programs for Girls
Bibliographic record
Abstract
This dissertation explores possibilities for environmental citizenship for girls. When environmental education emerged as a field of study in the 1970s, it articulated an environmentalism for young people based in the language of citizenship. However, environmental justice and feminist environmental education researchers have pointed out that this citizenship was homogenized, with little consideration given to gender, race, class, and sexuality, and that this citizenship was based on obedience to normative environmental prescriptions rather than on democracy and justice. At the same time, girls are often excluded from the vocabularies of citizenship because of their age, gender, and other intersecting factors, and their marginalization has been exacerbated by the myriad of programs for girls which, since the 1990s, have been empowering them with the message that they must change themselves rather than struggle for their social rights. This dissertation argues for a feminist project of environmental citizenship that politicizes gender and the intersecting categories of difference in girls lives, and also taps into environmental educations democratic potential to argue that girls need to be exposed to possibilities of social transformation and justice. \n \nTo bring gender and girls into environmental education, this dissertation rests on evidence gathered in field observations, interviews, and focus groups conducted with three environmental education programs for girls: the Girl Guides of Canada-Guides du Canada (Toronto), Green Girls (New York City), and ECO Girls (Ann Arbor), to demonstrate that gender, race, and class matter in girls access to the sciences, the outdoors, and environmental programming. Using a feminist environmental justice lens, it assesses each of the programs different models of ecological citizenship, arguing that an intersectional perspective and an openness to analyzing power, privilege, and difference generate more robust environmentalisms and ecological citizenships for girls. Specifically, the research considers that individual approaches to empowerment will not achieve the kinds of social change that are necessary for gender equality and environmental justice, and that forms of public engagement that are rooted primarily in service, leadership, and civic-mindedness as opposed to activism, advocacy, and collective mobilization are alone not enough to expose girls to the possibilities of full citizenship, social transformation, and democratic engagement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".