Corpus linguistics is not just for linguists
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
Purpose The purpose of this paper is to generate awareness of and interest in the techniques used in computer-based corpus linguistics, focusing on their methodological implications for research in library and information science (LIS). Design/methodology/approach This methodology paper provides an overview of computer-based corpus linguistics, describes the main techniques used in this field, assesses its strengths and weaknesses, and presents examples to illustrate the value of corpus linguistics to LIS research. Findings Overall, corpus-based techniques are simple, yet powerful, and they support both quantitative and qualitative analyses. While corpus methods alone may not be sufficient for research in LIS, they can be used to complement and to help triangulate the findings of other methods. Corpus linguistics techniques also have the potential to be exploited more fully in LIS research that involves a higher degree of automation (e.g. recommender systems, knowledge discovery systems, and text mining). Practical implications Numerous LIS researchers have drawn attention to the lack of diversity in research methods used in this field, and suggested that approaches permitting mixed methods research are needed. If LIS researchers learn about the potential of computer-based corpus methods, they can diversify their approaches. Originality/value Over the past quarter century, corpus linguistics has established itself as one of the main methods used in the field of linguistics, but its potential has not yet been realized by researchers in LIS. Corpus linguistics tools are readily available and relatively straightforward to apply. By raising awareness about corpus linguistics, the author hopes to make these techniques available as additional tools in the LIS researcher’s methodological toolbox, thus broadening the range of methods applied in this field.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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