ANTi-History, relationalism and the historic turn in management and organization studies
Bibliographic record
Abstract
Purpose This paper develops and provides insights on how researchers can use ANTi-History with a focus on one of its constitutive facets, relationalism. The purpose of this paper is to, first, develop a central facet of ANTi-History called relationalism and to outline how researchers interested in doing organizational history can use ANTi-History insights to undertake relational histories. Design/methodology/approach The authors propose four phases of the historic turn literature and situate ANTi-History and relationalism as an outcome of the fourth phase. The facet of relationalism is then explained and explored through five types of relations that the authors suggest act as sites of oscillation, where the past becomes (an immutable) history. Findings A central implication of the paper involves disrupting conceptualizations of the past and history as fixed. Instead, history is explained as a relational outcome of its constitutive social and political relationships. Originality/value The paper theoretically develops ANTi-History and relationalism while providing practical implications and tools for researchers to use it. Researchers are introduced to the notion of the site of oscillation. They are encouraged to focus their attention on five sites of oscillation: past-history, actor-network, human-nonhuman, researcher-traces of the past, and historical inscription-reading formation. These sites of oscillation are places where politics is at play and history is shaped or transformed.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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; 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".