Bibliographic record
Abstract
Bias is an ambiguous term, defined in different ways. In conventional usage, it indicates unwarranted prejudice. However, in health research, the notion that bias is invariably bad is biased. Although research bias is an error that is always harmful, researcher bias is a tendency to think in a particular way that may obscure or illuminate attempts to address research questions. Researcher bias begins with pre-judgements whose continuing evaluation infuses the subjectivity of researchers as persons who are socially situated in health sciences focusing on human subjects. Two sets of conditions can make this bias in health researchers useful. The first is volume control. Researchers can vary the loudness of their own and other voices in different research environments. The second condition is smart working. It balances researcher bias against analytic thinking to work creatively with irregularity and uncertainty. Thus, health researchers need to bring their biases to consciousness. A dialectical approach can then engage the biases as conversational partners to innovate health policy that is informed by principles including transparency, good faith and tolerance. Less critical than whether researchers are biased is whose interests their bias serves given their positionality and role.
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 | 0.763 | 0.745 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.016 | 0.224 |
| Scholarly communication | 0.043 | 0.069 |
| Open science | 0.015 | 0.032 |
| Research integrity | 0.030 | 0.062 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".