What does “Diversity” Mean for Public Engagement in Science? A New Metric for Innovation Ecosystem Diversity
Bibliographic record
Abstract
Diversity is increasingly at stake in early 21st century. Diversity is often conceptualized across ethnicity, gender, socioeconomic status, sexual preference, and professional credentials, among other categories of difference. These are important and relevant considerations and yet, they are incomplete. Diversity also rests in the way we frame questions long before answers are sought. Such diversity in the framing (epistemology) of scientific and societal questions is important for they influence the types of data, results, and impacts produced by research. Errors in the framing of a research question, whether in technical science or social science, are known as type III errors, as opposed to the better known type I (false positives) and type II errors (false negatives). Kimball defined "error of the third kind" as giving the right answer to the wrong problem. Raiffa described the type III error as correctly solving the wrong problem. Type III errors are upstream or design flaws, often driven by unchecked human values and power, and can adversely impact an entire innovation ecosystem, waste money, time, careers, and precious resources by focusing on the wrong or incorrectly framed question and hypothesis. Decades may pass while technology experts, scientists, social scientists, funding agencies and management consultants continue to tackle questions that suffer from type III errors. We propose a new diversity metric, the Frame Diversity Index (FDI), based on the hitherto neglected diversities in knowledge framing. The FDI would be positively correlated with epistemological diversity and technological democracy, and inversely correlated with prevalence of type III errors in innovation ecosystems, consortia, and knowledge networks. We suggest that the FDI can usefully measure (and prevent) type III error risks in innovation ecosystems, and help broaden the concepts and practices of diversity and inclusion in science, technology, innovation and society.
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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 | 0.034 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| 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".