The BIAS FREE Framework: A Tool for Science/Technology and Society Education to Increase Science and Risk Literacy
Bibliographic record
Abstract
Abstract Many academic and non-academic educational efforts are positioned at the intersection between science and/or technology and society with the purpose of increasing the literacy of students and others on the societal impact of science and technology and the ability of students to contribute to the academic and non-academic discussions around the societal impact of science and technology. To become risk literate of the social and other consequences of scientific and technological advancements is a critical aspect of scientific literacy. To be risk literate and to be able to identify biases in risk narratives is important for risk governance and risk communication, especially as it relates to marginalized groups. This chapter introduces the reader to the BIAS FREE Framework (Building an Integrative Analytical System for Recognizing and Eliminating InEquities, BFF) a tool developed for identifying and avoiding biases that derive from social hierarchies by posing 20 analytical questions. The premise of the chapter is that the BFF is a useful tool for educational efforts to enhance risk literacy of scientific and technological advancements including their risk narratives.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".