Topic modelling is a means to an end: On topic modelling in corpus linguistics and discourse analysis
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
Topic modelling (TM) is becoming an increasingly popular method in the corpus linguistics toolbox, especially when researchers are grappling with a large corpus and want to derive insights for a discourse analysis of the data.Following on from Bednarek's discussion in this issue, I would like to draw attention to three specific aspects of TM that should be considered when applying it.The first issue concerns the so-called 'black box' nature of the method.Researchers may apply TM without fully grasping the underlying principles, especially when it comes to parameters.Fundamentally, the criticism is that TM is technically difficult.My view is that it is not more technically challenging than, for example, keyword analysis, which can be computed using different statistics (Gabrielatos, 2018) and which corpus linguists apply, presumably, with full awareness of the possible options.It is not unreasonable to ask a researcher to study the principles behind TM or, as Bednarek suggests, to work collaboratively with somebody who does.Each step in TM is relevant to the results, including what kind of normalization is applied to the data, whether lemmatization or stemming is chosen, and whether and which stop words are removed.As an example, in Rao and Taboada (2021), we removed a standard set of stopwords.We performed relative pruning, to remove both common words (because they occur across all documents) and rare words (because they are unlikely to be representative of common topics across the data).Additionally, given that we were working with news stories, we also removed words related to news (say, report, story, press, news), social media and URLs (post, tag, inbox, https, href).Since those words were so frequent across all articles, they were not meaningful and removing them
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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