Cures for the heart: a poetic approach to healing after loss
Bibliographic record
Abstract
Writing creatively, expressively, and reflectively to aid the grieving process is founded on the idea that in order to survive and thrive after loss, personal meaning must be made of what has been suffered. The individualisation and secularisation of society has put the onus of meaning making on the individual while an abiding reservation about speaking openly about death in Western culture complicates this task. In this article we examine how metaphors are used to make sense of loss and its ensuing emotions and how they help the writer move from a ‘first’ (i.e. anguish, pain) to a ‘second’ story (i.e. healing). We argue also that evoking the beloved through writing is part of healthy grieving. We conclude that nudging, thread and crystalising metaphors as well as ‘poetic conversations’ – that help locate and position oneself in relation to the loss – together make constructing new life-giving (i.e. second) stories possible. The safety and privacy of writing is also key in making space for the voice of the bereaved. We note that grieving individuals ‘write about’ and ‘write to’ and thus evoke the person who has died. In this paradoxical way the bereaved face the reality of death by retaining the loved one.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".