Bibliographic record
Abstract
While progressing through my ongoing cancer treatments, in particular the reflection or re-reflections guided by Richard Kearney’s hermeneutic wager. I prefer a wager over the traditional cancer metaphors because it replaces the blatant harshness of a battle in a war. I am not saying in any way of form to be a passive observer during one’s cancer treatment journey, but to replace the winner-looser paradigm with carnage associated with war, shrapnel dismembered bodies unrecognizable to themselves and others. This does not mean that I am not standing up to and confronting cancer with vigor and intensity. The wager offers dignity during participation where all the “cards” are delt from both the cancer and the treatments with the integrity of the whole person who is living with cancer with their healthcare team and family play together as a community to successfully support the wager’s cause. The wager is respectful and addresses the risks involved and is fully c onscientious of outcomes as an unpredictable event. This wager is comprised of five reflections and/or conversations to engage in that I have adapted to learn about living with cancer and its treatments. The hermeneutic wager has five points of reflection: imagination, humility, commitment, discernment, and hospitality. These will be used to provide examples of how to offer insight into one’s experiences. Through these kinds of reflections on cancer, uncertainty can help us develop wings for the journey into the unknown uncertainty that often a diagnosis of cancer requires.
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 | 0.046 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".