Organizational intelligence dismounting barriers prioritization: A real-world case study
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
Organizational intelligence plays an important role developing business units and organizations. Understating the barriers surrounding an organization helps us take possible actions to remove any issues. In this paper, we present an empirical investigation to find barriers in university located in Province of Semnan, Iran. The proposed study of this paper first detects important barriers and then prioritize them using analytical hierarchy process. Based on the results of this paper, structural barriers are considered as the most important issue follows by legal barriers, cultural and executive barriers. The results of our survey indicate that lack of organizational knowledge management relation with daily activities, project complexity, lack of the knowledge exchanging and sharing in the organization, lack of suitable business context and absence of a documented program for the organizational intelligence dismounting are among the most important barriers according to our AHP implementation results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it