Corpus-based discourse analysis: from meta-reflection to accountability
Bibliographic record
Abstract
Abstract Recent years have seen an increase in data and method reflection in corpus-based discourse analysis. In this article, we first take stock of some of the issues arising from such reflection (covering concepts such as triangulation, objectivity/subjectivity, replication, transparency, reflexivity, consistency). We then introduce a new ‘accountability’ framework for use in corpus-based discourse analysis (and perhaps beyond). We conceptualise such accountability as a multi-faceted phenomenon, covering various aspects of the research process. In the second part of this article, we then link this framework to a new cross-institutional initiative – the Australian Text Analytics Platform (ATAP) – which aims to address a small part of the framework, namely the transparency of analyses through Jupyter notebooks. We introduce the Quotation Tool as an example ATAP notebook of particular relevance to corpus-based discourse analysis. We reflect on how this notebook fosters accountability in relation to transparency of analysis and illustrate key applications using a set of different corpora.
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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 | 0.196 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| 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".