Coming to Terms: A Discourse Epistemetrics Study of Article Abstracts from the Web of Science.
Bibliographic record
Abstract
This study investigates the relative power and characteristics of a set of social and epistemic terms to distinguish among disciplines of research article abstracts, using a corpus of 928,572 abstracts from 13 disciplines indexed by Web of Science in 2011. Applying the machine-learning approach to discourse epistemetrics using a sequential minimal optimization (SMO) algorithm, and a feature set of terms derived from Hyland’s (2005) metadiscourse studies per Demarest and Sugimoto (2014), the current paper reports subsets of terms that best (and least) distinguish among disciplines, finding that the terms least able to distinguish among disciplines are rarely used and overwhelmingly adjectival or adverbial markers of authorial attitude, reflecting personal positioning, while terms best able to distinguish disciplines are mostly verbs frequently used as engagement markers, framing the generation of knowledge for the readership in ways that are standardized within disciplines (while varying among them). We plan to analyze the findings of the current research-in-progress from discipline-based as well as term-based perspectives, incorporating both into a two-mode network, as well as incorporating finer grained data for specific specializations to compare with the current higher-level disciplinary findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.033 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".