Teaching the Possible: Justice-Oriented Professional Development for Progressive Educators
Bibliographic record
Abstract
Providing justice-oriented professional development for progressive educators has historically been a site of tension. To address this, The Progressive Education Network (PEN), the leading professional organization of progressive educators in the United States, brought together over 800 educators for its 2015 National Conference, titled “Teaching the Possible: Access, Equity, and Activism!” This article documents PEN’s framework for facilitating an opportunity for educators to engage in dialogue about areas of social injustice throughout education and within their own schools. Findings derived from a discourse analysis of workshop abstracts published in the conference program suggest that the conference provided professional development in three areas: 1) workshops were designed by teachers to share useful methodologies relevant to the conference theme with other teachers; 2) workshops encouraged attendees to critically examine how problematic issues in education are commonly understood, then reframe them to consider the issues from different perspectives; 3) doing so gave rise to an understanding that in order to imagine innovative solutions to systemic problems, one must first be able understand how different groups of individuals experience the problems. This analysis establishes that by aligning the conference with a critical, justice-oriented theme, the workshops were designed to provide attendees with opportunities to investigate their own roles in producing, changing, and interpreting socially-just learning and teaching in their own school contexts. This is important because it advances the study of equitable access to progressive pedagogy, while at the same time utilizing Desimone’s (2009) framework for judging effective professional development for teachers.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".