Samantha Adams Festschrift: Sam Adams and the Social Construction of Technology and Health—Implications for Biomedical Informatics
Bibliographic record
Abstract
Clinical informatics, as an applied subdiscipline of biomedical informatics (BMI), must expertly use concepts from BMI core disciplines to design and deploy technologies to improve human health. The core disciplines include, among others, computer science, medicine, and social science. Applying social science concepts is perhaps the most challenging because issues related to people and organizations often have many interpretations, explanations, and solutions. Sam Adams, a social scientist who enjoyed engaging the most complex theoretical domains, made valuable contributions to thorny social problems at the intersection of technology and people. In this article, we argue that her work and the theoretical topics she chose for emphasis should be more widely consumed by practitioners in clinical informatics. As an example, we focus on social constructionism, one general theoretical topic she favored. Sam used social constructionism to explore domains relevant to today's practicing clinical informaticist, including cybersecurity and social media.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".