Bibliographic record
Abstract
Financial reports are considered as one of the most important accounting system products.One of their major goals is providing the information required for evaluating the performance and profitability of the economic firms.In addition, the investment does not occur spontaneously, but they should be identified or created.Various types of investment opportunities may derive from different levels of the company.Some investment opportunities might be provided by senior management of the organization or by members of the board.The senior management involvement in providing investment opportunities is often limited to management measurement such as developing the company activity through financial policies and entering into new markets, given that investment opportunities cause allocation of financial resources to earn income or reduce the costs.Hence, regular and principal financial policies can be implemented for investment opportunities by company.The goal of this research is to evaluate the relationship between the quality of accounting information, corporate governance efficiency and selection of investment in the manufacturing listed companies in Tehran Stock Exchange since 2012 to 2016.This research is applied in terms of objective and post hoc in terms of reasoning and inferring.A total of 106 companies were selected as the research sample and the relationship between the variables was evaluated using the regression.The results suggest that companies with higher quality accounting information would likely invest in longer term.The impact of increasing the quality of accounting information on selection of long-term investment with the external governance environment is not strong.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.968 | 0.966 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".