Bibliographic record
Abstract
I was raised in North America ‐ a culture and society in which the education emphasizes knowledge about science, its methods and its principles. The scientific method of understanding, coupled with the rudimentary knowledge that I was taught in high school biology, resulted in my conceptualization of pain as an objective truth. Pain, as I believed for a long time, was a bodily sensation with an expression that was more or less universal; to me, pain was simply the sensation that the brain experiences as a response to noxious stimuli. The pain sensation protects us from things that can hurt us; it is a warning sign that something in us is physically amiss. Thus, everybody physically reacts to the touch of a flame or experiences abdominal pain when there is appendicitis. Even now, in medical school, the pain education that I have received so far has only involved the physiology or mechanics of pain. Pain, as a physiological condition, operates independent of cultural context. However, in considering the experience of pain that my grandmother has endured, I realize that pain is much more than a mechanical bodily sensation effected by the nervous system in response to stimulus. Pain is a human experience, and as such, it is highly individualized and subjective. The proper diagnosis, care and treatment of pain necessitate a holistic understanding of pain, its physiology and its context (1).
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".