Stories from and of the field: Developing teachers’ discursive practices of science
Bibliographic record
Abstract
Graphs and data tables play a central role in the formation and communication of scientific findings. Competent graph users interpret graphs by understanding the limitations of the representing role they play and other “real world” factors that may influence depicted relationships, using personal experience to contextualize unfamiliar graphs. This suggests that improved competency with using data inscriptions (i.e., data tables, graphs, maps, drawings, illustrations, and pictures) develops as one accumulates a repertoire of research stories that can be used to support interpretations of graphs and data. This study examines the discursive practices of teachers—how they talk, what language they use, and what they gesture towards—while discussing academic posters representing the research work of field biologists they did fieldwork with. The data suggest that the teachers developed rich “stories” drawn from their field experiences, which they used to describe their participation and to contextualize the findings that emerged from the field study. However, despite a preponderance of graphs and tables on the posters, they made few direct references to them. We suggest this occurred because the teachers had participated almost exclusively in the data collection aspects of the research and not in the generative claim-making part of the research. Nevertheless, the teachers’ narrative stories of their fieldwork suggest that they appropriated many of the discursive and research practices of scientists through developing their own stories-from-the-field—“Stories of Me”. They can relate these stories to their own students as firsthand narratives demonstrating nuanced understandings of the practices of real-world research and also use them as a foundation for planning inquiry activities for their own students. We conclude that more participation in the generative, claim-making aspects of science research might further enhance the ways in which teachers discuss research and research findings.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".