The DDI, ESK, and ME: Troubling the Epistemology of the Dominant Discourse on Indoctrination via Feminist Epistemologies of Situated Knowledges
Bibliographic record
Abstract
INTRODUCTION AND OVERVIEW The maxim “fish and houseguests begin to smell after three days” could apply to the problem of indoctrination, which seems to have outstayed its welcome by several decades. In spite of hundreds of papers, articles, and books on the subject in the field of philosophy of education, this bothersome term refuses to go away and the aroma lingers, as does the crucial question that the discourse purports to resolve: How can acceptable education be distinguished from unacceptable indoctrination? Although the literature is dominated by those who argue that indoctrination involves teaching such that students develop a stunted rationality, or such that they come to hold beliefs in a nonevidential fashion, this prescription has not served educators well. After a century of rancorous debate on the subject during which virtually all contributors argue that indoctrination is a form of unacceptable education, my research reveals not a single recorded instance where this dominant understanding of the term has led to the identification of specific “indoctrinators,” and/or moved them to change their approaches to teaching. I would suggest letting the matter rest, were it not for educators’, students’, and theorists’ continuing use of the term, especially in relation to such contentious contemporary issues as the inclusion of “creation science” and “intelligent design” in school curricula.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.091 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| 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 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".