The Language of Altruism: Corpus-Based Conceptualization of Social Category for Management Sociology
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.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".