Chapter 10 Epilogue: Living Revolution, Learning Revolution, Teaching Revolution
Bibliographic record
Abstract
After many iterations, the title of this book became, based on a suggestion from our series editor Tony Green, Educating from Marx: Race, Gender, and Learning . It is a simple and astute title through which Tony inadvertently returned us to the roots of this project. In the fall of 2006 a reading group began in the adult education and community development program at the Ontario Institute for Studies in Education at the University of Toronto. We came together for various reasons, the first of which was to read original texts by theorists of influence in the field of adult education. Marx, and those who followed after him, came to the forefront of this discussion given our interest in the critical/radical tradition of the field. Over time, the group coalesced around a central problematic: how to formulate a theoretical framework, drawing on anti-racism, postcolonial studies, feminism, and dialectical historical materialism, through which we could better understand the particular historical moment in which we live. We have asked ourselves a deceptively simple, but not simplistic, question that has guided our work: if we look through this framework, what do we see? At the conclusion of this exploration, we have to turn to another of these “easier said than done” propositions: how do we teach it? This epilogue serves as a reflection on this process with an emphasis, however, on what it means to think and teach from the Marxist-feminist ethical perspective we have elaborated in this collection. As we write, in the final harried days of manuscript production, we are deeply distracted by two pressing, important political events in the world around us. First, and most visibly, the Arab Middle East is gripped by a powerful wave of revolutionary passion. This most recent
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".