Bibliographic record
Abstract
Epistemology is the main branch of philosophy that studies the nature of knowledge, but how is new knowledge created? In this perspective article, I introduce a novel method of knowledge discovery that synthesizes online findings from current and prior research. This web-based knowledge synthesis method is especially relevant in today’s information technology environment, where the research community has easy access to online interactive tools and an expansive selection of digitized peer-reviewed literature. Based on a grounded theory methodology, the innovative synthesis method presented here can be used to organize, analyze and combine concepts from an intermixed selection of quantitative and qualitative research, inferring an emerging theory or thesis of new knowledge. Novel relationships are formed when synthesizing causal theories—accordingly, this article reviews basic logical principles of associative relationships, mediators and causal pathways inferred in knowledge synthesis. I also provide specific examples from my own knowledge syntheses in the field of epidemiology. The application of this web-based knowledge synthesis method, and its unique potential to discover breakthrough knowledge, will be of interest to researchers in other areas, such as education, health, humanities, and the science, technology, engineering, and mathematics (STEM) fields.
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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 | 0.073 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".