How Company Characteristics Influence Measurement Practices and Disclosure Level Prescribed within IAS 41
Bibliographic record
Abstract
This research paper describes the accounting practices of Jordanian companies engaged in agricultural activities, and identifies the influence of company characteristics on measurement practices related to asset pricing and level of disclosure required by IAS 41. Company characteristics were considered as: size, intensity of biological assets (BA), level of international activities, and audit for the Big Four. Dependent variables were considered measurement practices related to valuing BA as well as resultant harvest and disclosure level, the latter being measured by mandatory and voluntary disclosures. The entire population of companies that include one or more agricultural activities in their purposes and are considered reporting companies formed the research sample, giving a total of 259 companies. The findings revealed that both intensity of BA and level of international activities have a positive impact on measurement practices. Audit for the Big Four was the strongest variable influence, the overall disclosure level prescribed by IAS 41, followed by the level of international activities variable. However, the intensity of the BA variable affects only the overall disclosure level for companies that measure their BA based on the cost method. Firm size was found to have no influence on either measurement practices or disclosure level. The key value of this paper is its examination of the role of company characteristics on measurement practices and level of disclosure required by IAS 41 in the context of Jordanian companies. Through this examination, this study is helpful to standards setters and regulators who obligate and issue the financial regulation and reporting standards at a national or international level, supporting their understanding of measurement and disclosure practices adopted in agricultural companies in the developing country context of Jordan.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".