Gender and ethically relevant issues of visualizations in the life sciences
Bibliographic record
Abstract
Here moral problems created by the use of constructive imaging technologies within the life sciences are discussed. It specifically deals with the creation of dichotomies, such as gender, race and other differences, created and manifested through the contingent use of scientific and computational models and methods, channelling the production process of scientific results and images. Gender in technology studies has been concerned with destabilizing essentialist and dichotomous coconstructions of gender and technology. In the technological construction process gendered social constructions of stereotypes and inequalities both of the technological models and of the presumptions in life sciences become structural properties of the artefacts, again flowing back into the seemingly objective results and knowledge of the life sciences. Here we will deal with the construction of gender differences via biomedical imaging and the creation of norms in atlases. Additionally, the de-contextualized images, showing idiosyncratic selections and reducing complexity are used to popularize gendered assumptions about biological facts.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".