The Language of Altruism: Corpus-Based Conceptualization of Social Category for Management Sociology
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
Management sociology poses the problem of the quantitative interpretation of qualitative research. The article deals with the corpus-based method, which can be considered as one of the solution tools. Based on ‘grounded theory’ methodology (Strauss & Corbin, n. d.) and partly debating with conceptual analysis (Sartory & Goertz, n. d.), we propose to elaborate a definition of the concept using quantitative research. The authors identified useful areas of corpus linguistics in the analysis of social and management phenomena and distinguished between corpus linguistics and sociological content analysis methods:- Direct appeal to the everyday use of the language increases the objectivity of the research;- A corpus provides a large quantity of representative data; - The possibility of diachronic and synchronic comparative studies; - The method itself is not time-consuming and expensive.We chose the category ‘altruism’ as an example to demonstrate the possibilities of the method. The analysis shows features in the representation of altruism in Russian that the field of management sociology needs to address for the preparation of questionnaires, interview guides and transcript analysis.
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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 | 0.002 | 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 | 0.001 | 0.006 |
| 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