What warrants our claims? A methodological evaluation of argument structure
Bibliographic record
Abstract
Abstract The process of justifying a generalized theoretical conclusion from a specific empirical analysis continues to elude us. In this article, we suggest that this stems from an incomplete understanding and specification of how arguments are structured. Most importantly, in addition to empirical data, a generalized conclusion hinges on the application of various rules and principles of reasoning that British philosopher Stephen Toulmin labeled warrants. In this article, we apply Toulmin's model of argument structure to empirical management research by examining in particular the roles of four types of warrants: theoretical, inferential, procedural, and contextual. Based on our analysis, we suggest that making warrants and their backings explicit paves the way toward a more comprehensive understanding of how arguments are structured and how claims are justified. Importantly, an examination of warrants reveals that the choices researchers make are not limited to matters such as choosing the research topic or a particular research design, but they also extend to how we produce our claims. If we wish to understand argument structure, we must understand these choices.
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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 | 0.287 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".