Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this research is studying the relationship between organizational intelligence and organizational agility among experts and managers of real estate registration office of Fars province.The Method of this research is practical-descriptive and the population includes 742 experts and managers of real estate registration office of Fars province.In as much as the population was limited, clustering sampling method was used and 268 questionnaires were distributed and of these, 254 questionnaires were completed and returned.The research tool was the 49-questions questionnaire of Albrecht organizational intelligence and 36-questions questionnaire of organizational agility (adapted from Yusef et.al model) and validity was contention and its stability was achieved 0.81 using Cronbakh Alpha coefficient for organizational intelligence questionnaire and 0.89 was for organizational agility questionnaire.In order to analyze data in a descriptive level, table drawing, frequency and frequency percent were used and in inferential statistics, Pearson correlational coefficient and Regression coefficient were used.The findings show that there's a positive and meaningful relationship between organizational intelligence and agility.Also, there's another positive and meaningful relationship between all components of organizational intelligence except the tendency to change organizational agility.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.996 | 0.998 |
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 it