The Theme Progression Patterns in Popular Science Book Writing: A Systemic Functional Linguistics Approach
Bibliographic record
Abstract
Despite the proliferation of digital mediums such as documentary series, video essays, and science podcasts, popular science books are still the primary medium for promoting science to the public as an epistemic way to understanding and being aware of our natural world. That said, in the domain of systemic functional linguistics, there exists a dearth of studies investigating popular science books. Hence, this study aims to investigate the organization of popular science discourse from the perspective of Theme Progression. A 93,078-word corpus was collected and divided into two main science categories: hard and soft—three disciplines under each main category and six texts under each discipline. The analysis of the corpus followed a mixed-method design where a Theme-counting excel sheet, created by the researcher, was used to calculate the most occurring Theme patterns. The results of the analysis indicate that the hard science disciplines give a logical presentation of the scientific text that intends to unpack and explain the intricacy behind the scientific notion whereas the soft science disciplines focus on expository narrative to connect and relate the scientific text to the target reader. Given these writing behaviors between the two science categories, it is recommended that future SFL studies explore the potentially arising differences within the broader aspect of the popular science genre, for instance, to juxtapose books from articles in the way discoursal features (e.g., coherence and cohesion) are structured in the text.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".