Reduction in Practice: Tracing Husserl's Real-Life Accomplishment of Reduction as Evidenced by his Idea of Phenomenology Lectures
Bibliographic record
Abstract
Husserl claimed that reduction is the true starting point of phenomenological research, but to figure out how this deed should actually be accomplished has turned out to be a very challenging task. In this study, I explicate how Husserl accomplished reduction during his series of lectures entitled The Idea of Phenomenology. He does not state it explicitly, but what actually happened on the last day of the lectures can be seen as consistent with his descriptions of reduction as an act. Understood in this way, reduction is the model of how to do philosophy. The result of Husserl’s reduction is the correlation between appearance and “that which appears” or, to use Husserl’s later terminology, between noēsis and noēma. When this correlation is understood as an outcome of reduction and not as a result of an analysis, we, asreaders of Husserl, will be in a better position to avoid natural attitude in our interpretations.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 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".