Lives Penciled in, the Reality of Chronic Health Conditions and Trauma: Reflexivity, Health, and Shadowed Identities
Bibliographic record
Abstract
When living with chronic health conditions or experiences of trauma our lives can become perpetually penciled in. The use of the penciled-in metaphor means to arrange our time tentatively: a date, an appointment, a meeting, seeing a movie, or attending a class. In our technologically-driven world of electronic calendars where everything is entered electronically, the utility of the pencil and hand-written agendas have all but vanished. However, for the purpose of this article, the pencil provides a metaphoric common ground to learn about the totality of the disruption experienced by living with chronic health conditions and their residual trauma. The pencil is touchable, tangible and as a researcher and a person who lives with challenging health concerns, metaphors help me to create an understanding of the chaos of living a life in pain with cancer. This article is a person-centered account of the process of reflexive coping and self-processing of pain by a pain researcher and educator. This article focuses on the metaphor of penciled-in lives to provide a qualitative account of experiences of pain from chronic health issues and the trauma both physical and emotional it causes. This act of reflexivity becomes a personal examination of life. It reveals to me my beliefs, decisions, and practices before and during my hermeneutic journey and how these may have prejudiced my thinking and behaviors.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".