Bibliographic record
Abstract
Abstract Medicalization is the process by which conditions, for example, intellectual disability, hyperactivity in children, and posttraumatic stress disorder, become understood as medical disorders. During this process, the medical community often collectively assigns a label to a condition and consequently to those who would be said to have the disorder. We argue that there are at least two previously overlooked ways in which this linguistic practice may be wrongful, and sometimes, unjust: first, when the initial introduction of a medical label is done without the participation of those individuals who are being labelled, and second, when attempts by those individuals to renegotiate the labels are thwarted or otherwise rendered ineffective. In both cases, we argue, individuals are unfairly excluded from a linguistic practice that would be valuable for them to participate in. Furthermore, we argue that their exclusion depends in part on the authority of the medical institution to ignore their demands for participation. In making this case, we will propose the more general claim that participating in the linguistic processes of determining and renegotiating the words that will be used to describe oneself is an exercise of linguistic agency, a capacity that has both instrumental and intrinsic value.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".