“EFFECTIVE” GENEALOGICAL HISTORY: POSSIBILITIES FOR CRITICAL ACCOUNTING HISTORY RESEARCH
Bibliographic record
Abstract
This essay, following up on the recent Sy and Tinker [2005] and Tyson and Oldroyd [2007] debate, argues that accounting history research needs to present critiques of the present state of accounting's authoritative concepts and principles, theory, and present-day practices. It proposes that accounting history research could benefit by adopting a genealogical, “effective” history approach. It outlines four fundamental strengths of traditional history – investigate only the real with facts; the past is a permanent dimension of the present; history has much to say about the present; and the past, present, and future constitute a seamless continuum. It identifies Nietzsche's major concerns with traditional history, contrasts it with his genealogical approach, and reviews Foucault's [1977] follow up to Nietzsche's approach. Two examples of genealogical historiography are presented – Williams' [1994] exposition of the major shift in British discourse regarding slavery and Macintosh et al.'s [2000] genealogy of the accounting sign of income from feudal times to the present. The paper critiques some of the early Foucauldian-based accounting research, as well as some more recent studies from this perspective. It concludes that adopting a genealogical historical approach would enable accounting history research to become effective history by presenting critiques of accounting's present state.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.011 | 0.100 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".