Internal Connections and Conversations: The Internalized other Interview in Bereavement Work
Bibliographic record
Abstract
Much of the work of grief lies in the ways the bereaved learn to maintain connection to the deceased in their lives, while living alongside the physical absence of them. The theory of an Internalized Other Interview is that we carry within ourselves impressions, memories, beliefs, assessments, doctrines, and codes of those who have shaped our lives through relationship. This internalized community of commentators is active in our lives on a day-to-day basis, but when someone dies, their active voice in the dialogue is shifted to a perceived inactivity. However, I argue that, despite the physical absence of the other, the voice continues to resonate and interact in our formation of our worlds. How our loved ones live on inside us influences who we are in the world and in our bereavement. As a result of our research and clinical work, I have come to believe that the active interviewing of the deceased person as internalized in the bereaved can have powerful and healing effects. In this article, I share the results of the research related to this intervention, describe the history located in Internalized Other Interviewing, and offer a transcription of an Internalized Other Interview with a young man and his family who recently lost both his brother and father.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".