Review of <i>A Call to Action: An Introduction to Education,Philosophy, and Native North America</i> By CurryStephenson Malott
Bibliographic record
Abstract
In A Call to Action, Curry Stephenson Malott appeals to North American educators to acknowledge their essential role in the ongoing struggle for sustainable and ethical ways of living as humans. Malott joins a rising chorus of scholars who warn about a singular focus on the conflict between Indigenous and Western epistemologies (e.g., Glen Aikenhead's "Integrating Western and Aboriginal Sciences: Cross-Cultural Science Teaching" in Research in Science Education, 2001; Ray Barnhardt and A. O. Kawagley's "Indigenous Knowledge Systems and Alaska Native Ways of Knowing" in Anthropology and Education Quarterly 2005; and Ladislaus Semali and J. L.Kincheloe's editors' introduction to their 1999 What Is Indigenous Knowledge?: Voices from the Academy). He advocates instead for educators to recognize how Indigenous knowledge and Marxist analyses inform one another and together offer a path toward unification and transformation. To begin the process of transformation, Malott calls on educators to reflect on their responsibilities to the land on which they live and teach. In this way, educators may recognize that they are connected to one another and to the land, which may revolutionize their curriculum and pedagogy. In fact, Malott seeks to extend the appeal and reach of critical pedagogy by centering the issue of human relationship to land. Readers encounter some discussion about the role of humans as caretakers of the land and environment and a brief critique of how notions of resource scarcity breed a culture of fear and greed. We also read about a few examples of organic community development organized around natural resources like the Columbia River in the U.S. and the Lacondon Forest in the Mexican state of Chiapas.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".