Detecting the effect of main characteristics of accounting information on sustainable development at Al-Kharj Governorate
Bibliographic record
Abstract
The study aimed to discover the effect of the main characteristics of accounting information (AI) in achieving sustainable development (SD) in Al-Kharj Governorate by studying the characteristics of (AI) represented in relevance and reliability with independent variables and studying the dimensions of sustainable development (economic, social and environmental). The theoretical and applied study will use the descriptive and analytical approach. Data were collected through a questionnaire distributed to the study sample represented by business organizations in Al-Kharj Governorate. The data is analyzed using structural equation modeling with partial least squares. The expected results of the study are: The relevance of (AI) positively affects the economic dimension of (SD) in Al-Kharj Governorate, the relevance of (AI) positively affects the social dimension of (SD) in Al-Kharj Governorate, the relevance of (AI) positively affects the environmental dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) positively affects the economic dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) no effects on the social dimension of (SD) in Al-Kharj Governorate, the reliability of (AI) no affects the environmental dimension of (SD) in Al-Kharj Governorate.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".