Bibliographic record
Abstract
This dissertation is an ethnographic exploration into how translation is defined, taught and practiced in translational medicine. Based on fieldwork at translational research centres–one in Saint John and one in San Francisco–I confront a central tension between ways of understanding what translation is and how it should be performed in biomedicine. For some, translational medicine is simply another approach to commercializing research. For others, it is a novel way to bring researchers, governments, private companies and local communities together to shape a more democratic biomedical future. I argue that the latter perspective suffers from a lack of deep appreciation for the cultural, political and ethical complexities of translation. This is problematic because translational medicine implies the coming together of multiple languages, disciplines, bodies, technologies and institutions, and suggests a greater sensitivity to the unique and ever-shifting experiences of patients and their loved ones. Addressing these concerns, I put translational medicine in conversation with a variety of STS theories, and those from other disciplines that have long debated the messiness of translation. Experimenting with a deliberative approach to STS, I used participant observation, semi-structured interviews and online ethnography to actively debate what’s at stake in a translational approach to biomedicine. My unique role as STS scholar allowed me to challenge frameworks proposed for the near and distant future of translational medicine. Rather than an antagonistic posture, I worked, though often failed, to develop what Sarah Franklin (2013) calls “interliteracies,” or the “disciplined reading across disciplines” with my interlocutors. These interventions culminated with me combining STS and translational medicine in the design of a role-playing exercise for high school students at Princeton University. This exercise imagined a future where non-expert publics were more fully involved in determining what pieces of research can and should be translated. This dissertation contributes to increased calls for engaged approaches to STS that challenge and make decisions about how technoscience is practiced. By showing that translation is a shifting, non-linear and always unfinished process, this project opens space for STS to not simply say “it could be otherwise,” but actively be a part of making it otherwise. Keywords Medicine, Pedagogy, Cultural anthropology
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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; both teacher heads agree on what is shown here.
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".