The informing nature of talk & text: Discourse analysis as a research approach in information science
Bibliographic record
Abstract
ABSTRACT In Information Science (IS), as well as other disciplines, discourse analysis has been used to extend the range of contextual data gathered using other research approaches. This form of textual analysis can enrich our understanding of complex information practices and contexts, particularly in relation to the ways that society and individuals construct understandings of various phenomena. However, not all discourse analysis approaches are the same; linguistic, Foucouldian, and psycho‐social discourse analysis practices vary in their intent and their application. This panel will provide an overview of the discourse analysis methodology, including how the approach is conducted in various disciplines. By focusing on three projects by IS scholars that use discourse analysis, the range of data collection and analysis possibilities – including benefits and limitations of the approach – will be explored. The panel will also discuss how discourse analysis can be used in mixed methods studies, or with research participants engaged in other methods, to extend the research knowledge in the discipline.
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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 | 0.064 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".