Towards resilience: an approach from Edith Stein’s personal letters
Bibliographic record
Abstract
The term “resilience” suffers the fate of all terms that are used in an inflationary manner: They lose meaning and explosiveness. This article aims to give the term back its fullness by means of a phenomenological view according to Stein’s eidetic phenomenology. For this purpose, Edith Stein’s resilience, which distinguished her as a person, will be analysed. In addition, the way in which she fostered andshaped resilience in others will be highlighted. The concept of competence is used for the analysis. Four core competencies describe the sources of psychological resilience: self-competence, other-competence, fact-competence and meaning-competence, because resilience is formed in an adequate relationship to myself, to others or to otherness, to reality and to meaning. These competencies are each presented in Edith Stein’s life, in her support of others and in their relevance for the formation of resilience. The analysis keeps Stein’s philosophical or theological views in mind, but draws on her letters to individuals.One can state that in order to make sense of life, even of the adversities in life, the perspective of meaning is needed in all individual competences. Stein can even find the connection to meaning competence in the fact competence. Sie sees it also in the orientation towards others and in the understanding for them. Self-competence must also flow into meaning-competence; then we become able to face life adequately. This is ultimately the ability of the resilient person.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".