Toward a Social Practice Perspective on the Work of Reading Inscriptions in Science Texts
Bibliographic record
Abstract
In the social studies of science, visuals and graphical representations are theorized by means of the concept of inscription, a term that denotes all representations other than text inscribed in some medium including graphs, tables, photographs, and equations. Inscriptions constitute an intrinsic and integral part of scientific practice; their development and the development of science are tightly interwoven. A focus on inscription therefore goes together with the social psychological study of the cultural practices that embed inscriptions. Thus, scientists produce line graphs to convincingly show relationships; they use histograms to show distributions; or they combine multiple graphs to show contrasts or correlations between different entities and contexts. Inscriptions are also present in school science textbooks with great frequency and high school science activities, including teaching, demonstrations, and laboratory tasks. However, to read inscriptions successfully, students need to develop a special kind of literacy that is related to the use of inscriptions, which, in turn, is tied to the way in which these inscriptions are produced within an authentic science environment. The situated and highly contextualized nature of inscriptions renders them meaningful only within and through particular interpretive practices that are developed concomitantly with their production and use in authentic science settings, to which students may not have access in their daily school activities. Moreover, the representational (rhetorical) power of an inscription is related to the amount of information it may carry and to its level of abstractness, which are also proportional to its complexity and, thus, to the difficulty in reading it. In this article, we review the literature on reading inscriptions in science contexts from a social practice perspective as it has by and large emerged during the past two decades.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.014 | 0.121 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".