Analyzing the Nexus of Social Sustainability with Hierarchical Modification and Agency Viability among Commissioned Military Intelligence Operatives of Defense Intelligence Agency
Bibliographic record
Abstract
This paper substantiates the nexus of social sustainability with hierarchical modification and agency viability among commissioned military intelligence operatives of Defense Intelligence Agency. A descriptive study subsequent with a correlation configuration was utilized to determine the objective of this study. The statistical population was mostly commissioned military intelligence operatives of Nigerian Defense Intelligence Agency and a sample size of 300 commissioned military intelligence operatives; two hundred and forty two (242) men and fifty eight (58) women were arbitrarily picked utilizing the stratified random sampling methods. The data accumulation instruments were Social Sustainability Questionnaire, Standard Military Hierarchical Improvement Scale and Agency viability survey using Parsons’ Adaptation, Goal Attainment, Integration, Latency module (AGIL). Accordingly, the one-sample t-test data, Pearson correlation coefficient, linear regression analysis, F-test and independent t-test were applied to assess the data. The outcome of this study suggested that the contingency of social sustainability, hierarchical modification and Agency viability among commissioned military Intelligence operatives were highly significant and in demand. Additionally, social sustainability and hierarchical modification were altogether and emphatically related and social sustainability was essentially associated with subscales of Agency viability. Furthermore, social sustainability could predict 1.6% of the conflict in hierarchical modification and 2.9% of contradictions in Agency viability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".