Bibliographic record
Abstract
I examine the existing inequalities in healthcare and medicine in regards to gendered variation in treatment and access to medical resources or information. My goal of this independent research project is to identify specific areas of healthcare where there is variation in the quality of care received by patients as a result of their gender identity. I have employed an online survey that was distributed to a variety of platforms, with all genders encouraged to participate. The responses from this anonymous survey were compiled and examined in order to identify specific trends and areas of concern within the healthcare system. The survey predominantly consisted of short answer questions that were scored based on severity of response, with those responses then sorted according to the gender identity of the respondent. Special attention was paid to instances of marginalization, unequal access to resources or medical information, and any other form of discrimination, where there was variation in the quality of treatment received in medical settings in response to the gender of the patient. The results from the survey were then examined in the context of existing literature which discusses inequality in healthcare and medicine, and a statistical analysis was performed based on the data obtained. Department: Biology Faculty Mentors: Dr. Katie Biittner and Dr. Monica Davis
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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".