Bibliographic record
Abstract
Purpose The purpose of this paper is to present a reflexive review of ANTi-History written as a reply to a critique by James Reveley, published in the Journal of Management History, called “Firm objects: new realist insights into the sociohistorical ontology of the business enterprise.” Design/methodology/approach Reveley’s critique of ANTi-History focuses on three aspects, namely, matters of ontology, actors and relationalism. Using the logic of ANTi-History, the author reviews each and offers a reply. Findings This paper demonstrates that ANTi-History is inspired by amodern thought. This condition negates the need and desire to classify social and physical objects in the study of history. Drawing on Actor-Network Theory, ANTi-History assumes that historical actors are heterogeneous, and the consequence is that both human and nonhuman actors should feature in the study of history. The focus, in using ANTi-History, should be in-between the human and nonhuman actors that make up the past and history. This is the premise of using a relational lens. Originality/value The review of ANTi-History is structured as a reply to critiques of the approach. In reflecting on these criticisms, the author realizes that ANTi-History has gotten beyond its originators. As one of those originators, the author inspired to continue to develop its strange potential.
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 | 0.036 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".