Quants and Poets: Advancing Methods and Methodologies in Business and Society Research
Bibliographic record
Abstract
Business and society research has increasingly moved from the margins to the mainstream. Although this progression has benefited from advances in empirical research, the field continues to suffer from considerable methodological challenges that hamper its development. In this introductory article to the special issue, we review how far our field has come in advancing methods and methodologies in business and society research. We also highlight the methods and methodologies covered by the contributors to this special issue and how they help address key shortcomings in our field. Finally, we suggest some promising research methodologies that can address important business and society research challenges going forward.
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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 | 0.102 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".