Bibliographic record
Abstract
ABSTRACT: This paper follows up on the discussion on “advising” the Financial Accounting Standards Board (FASB) about social and economic reality. It began with Lee (2006a), was commented upon in Macintosh (2006) and Williams (2006), and closed with a reply to both papers in Lee (2006b). All three authors criticized, in one way or another, the Financial Accounting Standards Board and the fashion in which it attempts to incorporate principle-based accounting standards into its conceptual framework (CF). The main thrust of these four papers is a critique directed toward the FASB, which has been more concerned with “comparability and consistency” than with “identifying improved ways of recognizing and representing social-constructed reality and truthful correspondence in the light of principle-based accounting standards” (Lee 2006a, 1). Thereby, Lee promotes Searle's (1995) theory of constructing social reality. The primary purpose of the current paper is to show that the methodology of the “onion model of reality” (OMR, developed in Mattessich 1991, 1995, and 2003) offers several advantages over Searle's (1995) approach. Above all, the results of the OMR are less confusing and much closer to accounting terminology as well as that of everyday language (e.g., saying: “The U.S. federal debt is a social reality,” instead of the cumbersome formulation: “The U.S. federal debt is ontologically subjective”—the text discusses additional advantages of the OMR). The backbone of the OMR is the fact that each reality level is endowed with its very own emergent properties, hence with its specific kind of reality.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".