The role of non-corporeal Actant theory in historical research
Bibliographic record
Abstract
Purpose This paper aims to study the role of non-corporeal Actant theory in historical research through a case study of the trajectory of the New Deal as one of the foremost institutions in the USA since its inception in the early 1930s. Design/methodology/approach The authors follow the trajectory of the New Deal through a focus on Vice President Henry A. Wallace. Drawing on ANTi-History, the authors view history as a powerful discourse for organizing understandings of the past and non-corporeal Actants as a key influence on making sense of (past) events. Findings The authors conclude that non-corporeal Actants influence the shaping of management and organization studies that serve paradoxically to obfuscate history and its relationship to the past. Research limitations/implications The authors drew on a series of published studies of Henry Wallace and archival material in the Roosevelt Library, but the study would benefit from an in-depth analysis of the Wallace archives. Practical implications The authors reveal the influences of non-corporeal Actants as a method for dealing with the past. The authors do this through the use of ANTi-History as a method of historical analysis. Social implications The past is an important source of understanding of the present and future; this innovative approach increases the potential to understand. Originality/value Decisions are often black boxes. Non-Corporeal Actants are a new tool with which to see the underlying inputs of choice.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".