Bibliographic record
Abstract
Some "diseases" appear to be recognized first by sufferers. At times these diseases may be disclaimed by medical doctors and elusive to scientific categorization and description. In these cases sufferers may organize themselves together in support groups and lobby for money to finance the discovery of diagnostic markers that would legitimate and medicalize the constellation of symptoms that they experience. Chronic fatigue syndrome is such a disease; and it is characterized by varied and changing symptomatology. Its diagnostic markers are in the process of being refined. Presently, its diagnosis primarily originates in reports of subjective experience of extreme fatigue. Often-times people diagnose themselves after attending a support group and find a doctor through a support group network who believes in the disease. Sometimes, people then return to their own family doctors with information and try to teach their doctors about what they believe to be the nature of their disease, its prognosis and treatment. Through such paths as described in the paper, patients become "experts": they may often know more about the illness than doctors and non-suffering others. This paper moves beyond the experience of chronic illness to describe the processes through which people seek confirmation and legitimation for the way that they feel and in a sense become the "experts."
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.107 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".