Financial accounting: an epistemological research note
Bibliographic record
Abstract
This research note is the result of the authors' reflections on epistemological issues in respect to the financial accounting field. From an epistemological perspective, this document attempts to trace the philosophical, historical, sociological, and discursive research perspectives that have guided academic research in the field of financial accounting. In order to do so, this document explores the distinctions and connections between accounting theory and accounting practice, which we believe is the first step towards understanding accounting as a scientific discipline. We analyze the theories underpinning financial accounting research, discussing its purposes, historic evolution, and scientific methods used. This document also discuss the sociological and discursive contexts of financial accounting in order to demonstrate that, like every other social science, accounting research is based upon assumptions about the nature of it players, or social networks. This document does not have the pretension to cover or close the discussion about all the pitfalls of this complex topic. In this sense, we try to document our analysis and draw some arguments in order to offer evidence for further discussion.
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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".