LaMa: a thematic labelling web application
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
Qualitative analysis of data is relevant for a variety of domains including empirical research studies and social sciences. While performing qualitative analysis of large textual data sets such as data from interviews, surveys, mailing lists, and code repositories, condensing pieces of data into a set of terms or keywords simplifies analysis, and helps in obtaining useful insight. This condensation of data can be achieved by associating keywords, a.k.a. labels, with text fragments, a.k.a artifacts. It is essential during this type of research to achieve greater accuracy, facilitate collaboration, build consensus, and limit bias. LaMa, short for Labelling Machine, is an open source web application developed for aiding in thematic analysis of qualitative data. The source code and the documentation of the tool are available at https://github.com/muctadir/lama. In addition to being open-source, LaMa facilitates thematic analysis through features such as artifact based collaborative labelling, consensus building through conflict resolution techniques, grouping of labels into themes, and private installation with complete control over research data. With the help of this tool and flow it enforces, thematic analysis becomes less time consuming and more structured.
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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