NARRATIVE RETICULATION — METHOD OF THE COMMUNITY-BASED SOCIAL HEALING
Bibliographic record
Abstract
The holistic approach of community-based social healing is aimed at creating and sensitive living of human connectedness — the surplus of interpersonal interaction, a way of supportive coexistence. Community, as the basis of this approach, should be understood as the "point of intersection" (or rather "center of gravity") of two intentions, the separation of which is conditional: to heal the community and to create a healing community. Highlighting the first emphasizes the direction to a special collective relationship. The name “community’ can be given to any group of individuals who are united by a certain characteristic - place of residence or work, preferences, common traumas, etc. However, such a group often does not have inner connectedness even in normal conditions. More crucial is that in protracted traumatic conditions connectedness tends to get lost; it intensifies the traumatic experience even more and needs healing — connecting ex nihilo. The "provoked" by supporting practices surplus of connectedness is a source of healing as a collective unity regardless of, or contrary to, the objective order of existence. Highlighting the second intention (to create a healing community) emphasizes the need for community for individual healing.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".