EXPLORING FEAR: ROUSSEAU, DEWEY, AND FREIRE ON FEAR AND LEARNING
Bibliographic record
Abstract
Fear is not the first feature of educational experience associated with the best‐known progressive educational theorists—Jean‐Jacques Rousseau, John Dewey, and Paolo Freire. But each of these important thinkers did, in fact, have something substantive to say about how fear functions in the processes of learning and growth. Andrea English and Barbara Stengel juxtapose the ideas of these thinkers in this essay for three purposes: (1) to demonstrate that there is a progressive tradition that accounts for negative emotion in learning; (2) to explore doubt, discomfort, and difficulty as pedagogically useful, with links to fear as both a prompt for and an impediment to growth; and (3) to suggest that teachers take negative affect into account in their pedagogical practice. In doing so, English and Stengel join with contemporary theorists in and out of education to recognize that affect cannot be left out of social theory and that understanding the play of emotion is an integral part of creating truly educational contexts and experiences. The authors' focus here is on fear in processes of learning.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".