Educational Implications of E. Fromm's View of "Ends": Reference to J. Dewey's Idea of the Means–Ends Relationship
Bibliographic record
Abstract
As one of the most prominent psychoanalysts working mainly in Germany, the USA, and Mexico in the 20th century, Erich Fromm often referred to American thinkers, including Emerson, Thoreau, James, and Dewey, and indicated the similarity between Freud's and Dewey's thought. Although recent scholarship overlooks this indication, Fromm highly valued Dewey as a one of the leading figures of humanistic ethics and respected his theory as well as criticizing it. The present study, therefore, highlights the philosophical relationship between Fromm and Dewey and explores Fromm's view of "ends" with reference to Dewey's idea of the means–ends relationship to better understand the implications of Fromm's ideas for education. The study first examines Fromm's interpretations of Dewey and confirms the similarities and differences in their theories. Subsequently, the study elucidates how Fromm's "science of man" creates "the model of human nature" from which "ends" are deduced. Further, this study confirms that Fromm transforms the very question that we must address on ends and reveals his alternative way of seeking ends. Finally, the study suggests that Fromm's view of "ends" has educational implications.
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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 | 0.005 | 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 | 0.006 | 0.027 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".