LaMa: a thematic labelling web application
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 opensource 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.
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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.133 | 0.044 |
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".