“Forgiveness is About Building Your Identity Again After the Transgression”: Narrative Identity and Turning Points in the Forgiveness Process
Bibliographic record
Abstract
Forgiveness is a multilayered process. However, there is little research on how individuals construct their narrative identity through self-positioning at turning points in the forgiveness process. The present study investigated these questions by interviewing 22 Finnish adults, applying McAdams’s life story interview method. Data-driven thematic narrative analysis demonstrated six turning points for the positioning self: (1) prologue: reflecting on the self from a distance, (2) the self gets help from others, (3) battling with the self, (4) the enlightened self, (5) the self initiates confrontation and (6) epilogue: the stronger future self. For the participants, these turning points were complex and profound experiences. Narrative turning points of forgiveness represented the many shades and phases of the forgiveness process that shaped participants’ positions and lives. The process was not linear and included stalled phases. Self-positioning moved from transgression to forgiveness, and in this process, the self’s agency varied from external to active. Participants described turning points in narratives of learning and empowerment with a strong protagonist who wants to move on in life. Turning points of forgiveness and self-positions may take different narrative forms in the future as individuals continue to narrate their forgiveness.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".