Bibliographic record
Abstract
When working with big data in science (research databanks, literature reviews) and everyday life (news aggregators), there is a need for mining, classifying and storing information. Information is defined as data in a processed form. The methodology of content analysis in its various forms, qualitative (manual coding), quantitative (words frequencies and co-occurrences) and mixed methods (creation of ad hoc dictionaries based on substitution), offers a tool to address this issue. Interest in content analysis emerged as early as in the 1970s, yet it remains relatively unknown outside of sociology, linguistics and communication studies. Content analysis allows converting qualitative data (texts, images) into digital format (vectors and matrices) and subsequent manipulating digital information using linear algebra, multidimensional scaling and other tools from natural sciences. The conversion into digital formal also paves the way to machine learning. Supervised machine learning looks particularly promising since it implies keeping focus on interpretation of data proper to interpretative sociology. Supervised machine learning is compatible with mixed methods content analysis. The existing program for computer-assisted content analysis (QDA Miner, Atlas TI, NVivo etc.) have several limitations. Restrictions on the number of their users (coders) refer to one of the limitations. The creation of on-line platforms for content analysis allows bypassing this and some other limitations. The idea of creating an on-line databank for qualitative data and a platform for content analyzing it is discussed. In contrast to quantitative data, qualitative research data is rarely available for secondary analysis.
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 | 0.075 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".