<i>Nacherzeugung, Nachverstehen</i>: A phenomenological perspective on how public understanding of science changes by engaging with online media
Bibliographic record
Abstract
It is widely acknowledged in science education that everyday understandings and evidence are generally inconsistent with the scientific view of the matter: "heartache" has little to do with matters cardiopulmonary, and a rising or setting sun actually reflects the movements of the earth. How then does a member of the general public, which in many areas of science is characterized as "illiterate" and "non-scientific," come to regard something scientifically? Moreover, how do traditional unscientific (e.g., Ptolemaic) views continue their lives, even many centuries after scientists have overthrown them in what are termed scientific (e.g., Copernican) revolutions? In this study, we develop a phenomenological perspective, using Edmund Husserl's categories of Nacherzeugung and Nachverstehen, which provide descriptive explanations for our observations. These observations are contextualized in a case study using online video and historical materials concerning the motions of the heart and blood to exemplify our explanations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".