Employee Change Agents: The Foundation for Effective Organizational Change
Bibliographic record
Abstract
This paper specifically looks at the process of organizational change and how employees can influence the outcomes. Organizations tend to underutilize their employees during times of change. An organization is basically a collection of people with the same objectives. Ultimately, I seek to understand how an employee change agent framework can function as an essential tool for controlling and analyzing organizational change. As a manager and an instructor of management, I believe that is the employee that essentially controls the success of any change in an organization. Employees should be charged with the ability to foster positive change by management, but more so, by themselves. It is the employees responsibility to make the change work in the organization, along with making changes in themselves to help improve not only the organization, but to improve their own working behaviors. All employers should be creating a learning organization by asking their employees to become responsible members of the organization. Allow your staff to be present at the table. Also empower your staff to become more aligned with the core mission of the organization. The more your staff believes they are making a difference for the organization, the more vested, accountable, and responsible they will become.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".