Linguistic Characteristics of Texts: Methodological Notes on a Missed Step in Critical Discourse Analysis
Bibliographic record
Abstract
Critical discourse analysis is a set of theoretical and methodological devices used to analyze and challenge how we construct reality by looking for meaning behind words. The process of conducting critical discourse analysis is complex and, in the field of nursing research, is often carried out without regarding the different dimensions entwined in discourse. In this vein, the dimension that concerns the linguistic characteristics of texts is a highly informative one, but all too often left out of study results by researchers in nursing. This article aims to present some methodological notes of our experience conducting an analysis of linguistic characteristics within a critical discourse analysis doctoral research. We discuss the theoretical and methodological reasons why these characteristics should be widely recognized as a fundamental dimension in the framework of critical discourse analysis. We propose general recommendations to make this dimension of analysis more easily accessible and encourage other critical researchers to include the analysis of linguistic characteristics of texts in their research.
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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 | 0.178 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.018 | 0.065 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| 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".