Book Review: Teaching About Gender Diversity: Teacher-Tested Lesson Plans for K-12 Classrooms
Bibliographic record
Abstract
Within current schooling contexts, more nuanced conversations regarding gender, gender identity, and gender expression are required to ensure educators and school policies are appropriately informed and safer school environments are established for gender diverse students (Airton et al., 2019;Davies et al., 2019).Teaching About Gender Diversity: Teacher-Tested Lesson Plans for K-12 Classrooms, edited by Susan W. Woolley and Lee Airton, addresses the need for accessible lesson plans for educators to become prepared to openly encourage a classroom climate that is supportive of gender diversity.Featuring lesson plans written by a range of experts involved in the K-12 school system -from teachers to higher education researchers -Woolley and Airton do an excellent job of providing educators with the tools to unpack their own biases and encourage supportive classrooms for students of all gender identities.Bridging theory and practice is often described as a challenge by practitioners.Yet, Woolley and Airton are clear in laying out the theoretical frameworks that inspire their text in accessible language (queer theory, transgender studies, postcolonial theory, and black feminist thought).The book follows a sequential order that is positioned in three sections for different grade areas: Elementary (K-5), Middle Years (6-9), and Secondary Education (10-12).Another strength of this text is its recommendation to "assume that there is always gender diversity in the room" (p.18).Addressing important provocations for educators in their introduction, reading the beginning portion of this text already leaves educators of all levels (early childhood education, elementary, secondary, higher education) with many wonderful questions and provocations that can immediately be put into their professional practices.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".