Surrounded by text: the meaning of health represented through the texts of life. An interpretative process
Bibliographic record
Abstract
Our omnipresent reflective worlds invite interpretation through the incalculability and types of text that surrounds us during our daily lives. We as human beings have no choice but to acknowledge this bombardment of texts as our obligatory and oblivious day-to-day engagements or be in denial of them because, as their meaning has not become relevant and interpreted within our lives. These texts of life will continue to appear regardless of whether through an interpersonal encounter within our taken-for-granted lives, as researcher through the recounting of a research a research participant’s lived experience of something, or of a form of “art” that uniquely somehow “speaks to us”. Patients, clinicians, and researchers are offered windows, images, narratives, metaphors, or other creative expression into the complex experiences that can be explored and interpreted to help understanding complex health conditions. Chronic pain and cancer an example of these but the text associated with these transcend their medicalization to include the ontological pain associated with the day-to-day distress these can create. This presentation offers the personal and academic reflections as a researcher and a person who lives with chronic pain and is currently undergoing a second round of chemotherapy for cancer. By finding a common ground of understanding the clinical and life experiences of living with chronic health conditions becomes mutually more accessible and may enhance the treatment of the person. The first step it to help create awareness that text and interpretation skills can benefit the clinician, the patient, and researchers.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.063 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".