Bibliographic record
Abstract
This work includes a foreword by John D Morgan, Professor Emeritus of Philosophy, Coordinator for Kings College Center for Education about Death and Bereavement, Ontario, Canada. This practical resource guides the reader though all aspects of the grieving process and offers thought-provoking and inspirational advice on support. With exercises, tips, and contacts for further assistance, "Finding a Sacred Oasis in Grief" provides a comprehensive understanding of this potentially difficult and complex topic. It examines different types of grief and various approaches, along with reference guides to particular religions and their traditions adopting a comprehensive, multi-faith approach. Pastoral care providers and religious leaders will find the unique, hands-on approach invaluable, as will members of support organisations and volunteer carers. It is also ideal for seminary and ministry students, counsellors, therapists and other care professionals. "Gives caregivers the tools to help dying and grieving persons face the best and worst that life has to offer. It is the worst, because death means the end of the attachments that make life worthwhile. It is the best, because it shows us what is truly meaningful and important in life. Mortality is a great gift if we have the knowledge and the courtesy to face it." - John D Morgan, in the Foreword.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".