Uses of content analysis in economic sciences: An overview of the current situation and prospects
Bibliographic record
Abstract
The article discusses the status of quantitative and qualitative data in economic sciences, as well as methods for transforming data into information and knowledge. Particular attention is devoted to content analysis as a set of methods for aggregating, processing and analyzing qualitative data; its forms (qualitative, quantitative and mixed methods) and uses by economists. Content analysis appears to be particularly suitable for non-orthodox economists because of their refusal to consider price as the only source of economic information. The content analysis of metadata of articles indexed in Web of Science and eLibrary suggests that Russian economists still have insufficient familiarity with the principles of content analysis and their applications to research compared with their Western counterparts. It is argued that the creation of on-line platforms for content analysis and on-line banks of qualitative data may become a trigger for changing this situation.
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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 | 0.044 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.028 | 0.031 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".