A theoretical framework for narrative explanation in science
Bibliographic record
Abstract
This paper deals with a number of conceptual and theoretical issues that underlie the proposal to employ narrative explanations in science education: What is narrative? What is explanation? and What is narrative explanation? In answering these questions, we develop a framework of narrative elements and characteristics of narrative explanations. Two possible examples of narrative explanation are presented and examined in light of the framework. This examination brings to light various conceptual and empirical questions related to the examples and to the larger issue of the use of examples like them in science instruction. The value of the framework lies partly in its power to point to such questions. The questions can guide a program of theoretical and empirical research into the psychological reality of the narrative form of explanation, the existence of narrative explanations in science, the use of narrative explanations in science teaching, and the nature and extent of the narrative effect upon which proposals for the use of narrative often are justified. © 2005 Wiley Periodicals, Inc. Sci Ed, 89:535–563, 2005
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".