Corpus-based discourse analysis: from meta-reflection to accountability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it