Topic modelling is a means to an end: On topic modelling in corpus linguistics and discourse analysis
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
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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 | 0.067 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.021 | 0.039 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".