Bibliographic record
Abstract
This Special Issue expands mobilities research through the idea of therapeutic mobilities, which consist of multiple movements of health-related things and beings, including, though not limited to, nurses, doctors, patients, narratives, information, gifts and pharmaceuticals. The therapeutic emerges from the encounters of mobile human and non-human, animate and inanimate subjects with places and environments and the individual components they are made of. We argue that an interaction of mobilities and health research offers essential benefits: First, it contributes to knowledge production in a field of tremendous social relevance, i.e. transnational health care. Second, it encourages researchers to think about and through functionally limited, ill, injured, mentally disturbed, unwell and hurting bodies. Third, it engages with the transformative character of mobilities at various scales. And fourth, it brings together different kinds of mobilities. The papers in this Special Issue contribute to three themes key for the therapeutic in mobilities: a) transformations (and stabilizations) of selves, bodies and positionalities, b) uneven im/mobilities and therapeutic inequalities and c) multiple and contingent im/mobilities. Therapeutic mobilities comprise practices and processes that are multi-layered and mutable; sometimes bizarre, sometimes ironic, often drastically uneven; sometimes brutal, sometimes beautiful – and sometimes all of this at the same time.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".