Exploring the Involvement of All Managers and Employees in Developing and Implementing Performance Management Systems in Canadian Public Sector Organizations
Bibliographic record
Abstract
This thesis investigated the extent of involvement of Front-line Employees and Senior- Mid- and Operational-level Managers in the conception, design, development and implementation of their Performance Management System (PMS) and outcomes from their involvement in four public sector organizations. Performance Measurement (PMe) and Performance Management (PM) have a lengthy history in the measurement and management of organizational and employee performance. Measuring performance in organizations began in the early 1800s when municipal performance data were collected and analyzed with the goal of increasing employee and organizational performance. PMe and Performance Management Systems (PMSs) originated following a general dissatisfaction with the traditional financial performance measures. A review of the literature revealed that even though many PMe systems and PMSs such as the BSC were originally developed for use in private sector organizations, recent pressures have resulted in the adoption of similar systems in public sector organizations. New and growing challenges, driven by changing demographics, deregulation, technological advances, free-trade, global economic change, changing public attitudes, emphasis on customer satisfaction and competition for qualified employees have led to greater demand for accountability and transparency in public sector organizations. These changes require public sector organizations to adopt private sector PMS initiatives such as Kaplan and Norton's (1992) Balanced Scorecard (BSC).
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".