Girls as Thriving Leaders: Cultivating a Community of True Belonging in an All-Girls School
Bibliographic record
Abstract
In an all-girls school, what conditions are required to authentically cultivate a community of true belonging? This inquiry explores what girls need in schools today to support each other now and as women. This Organization Improvement Plan is developed for an all-girls Canadian independent school. Challenges in maintaining true belonging stem from the systemic, patriarchal structure of education. This gap creates a culture of scarcity and competition, which leads to intragender microaggression. To implement change in this organization as an informal leader and teacher, I use a participatory-based approach that aligns with the feminist-transformative theoretical lens. The ethic of community and ethic of critique frameworks ensure that this inquiry goes beyond the narrative of the neoliberal definition of girlhood. To address this challenge, a skillset of competence, mindset of confidence, and heartset of connectedness must be present and continually developed as indicators of true belonging in all-girls schools today. I developed a conceptual model for leading, teaching, and learning to guide a multi-layered leadership practice in our learning community in order for true belonging to be possible. As a leader, my actions align with transformative, inclusive, and connective leadership styles. The chosen solution addresses true belonging by offering an interdisciplinary program that aligns with the conceptual model. This solution uses an iterative, cyclical framework for organizational development, with an emphasis on appreciative inquiry for positive change. The plans for implementation, monitoring and evaluation, and communication ensure active participation, opportunity for voice and choice, and reflection through generative dialogue.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".