Discovering Life through Loss and Grief
Bibliographic record
Abstract
In times of loss, communities of faith come alongside bereaved individuals to offer support. That support is often short lived. When the casseroles stop, grieving people feel isolated and underserved in communities of faith when pastors and community members are ill-equipped and unprepared to care for them.(2) The reason pastors and community members are ill-prepared to care for those grieving is historically, culturally, and theologically complex. Tackling such complexity is beyond the scope of this project. Implementing Occam’s Razor, where the simplest solution, with the least moving parts, suffices, enables me to address my NPO in a creative way.(3) For Christians, Jesus’ life serves as a model of how to live and love in this world. Philippians 2:5-11 provides a concise and foundational text of Jesus moving proximate to humanity by becoming fully human. Proximity to humanity necessitated navigating loss and grief, and being present with others as they do the same. What does it look like for people who follow Jesus to personally and communally move proximate to their humanity, so they can be present with the humanity of others? To address this question, I developed an 8-week, one-credit, graduate level spiritual formation course for Portland Seminary entitled “Discovering Life Through Loss and Grief.” Through the metaphor of pilgrimage and developing the spiritual practice of memento mori, “remember you will die,” students acknowledge their humanity by engaging with and integrating personal stories of loss and grief. (4) Course resources and format invite them to practice companioning one another in grief as they listen to each other’s stories of loss. Giving language to loss awakens us to the realities of what it means to be human. Speaking our stories reminds us we are not alone. In sharing, we discover new life emerges from death in profoundly unexpected ways. (2) This is my working NPO. It was shaped after assimilating responses from the required discovery session and one-on-one interviews, hosted in November 2019, for my project portfolio. (3) Farnam Street, The Great Mental Models, Vol. 1: General Thinking Concepts (Ottawa, ON: Latticework Publishing, Inc. 2019), 160. (4) Wikipedia, “Memento mori,” accessed January19,2022, https://en.wikipedia.org/wiki/Memento_mori.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".