Perceived managerial and leadership effectiveness within the Canadian public sector
Bibliographic record
Abstract
Abstract This study responds primarily to numerous calls for specific public management and public administration‐related research to better understand public leadership currently performed in an increasingly complex and ambiguous world. It also responds to calls in the human resource development (HRD) literature for more qualitative managerial behavior research. The inquiry explores perceptions of what behaviorally distinguishes effective managers from ineffective managers, as expressed by managers and nonmanagerial employees within a Canadian public utility company. It reaches for generalization by comparing the results against findings from equivalent qualitative managerial behavior studies carried out in three subareas of the British public sector. Using the critical incident technique (CIT), concrete examples (critical incidents [CIs]) of observed managerial behavior were collected from managers and nonmanagerial staff. The CIs (n= 530) were subjected toopenandaxialcoding to identify a smaller number of discrete behavioral categories (BSs).Selectivecoding of the identified BSs (n= 99) resulted in 16 positive (effective) and 12 negative (ineffective) behavioral criteria (BCs) being deduced. Over 92% of the Canadian BSs are convergent in meaning with over 81% of the compared British BSs. Consequently, they are likely to be generalizable to other subareas of the Canadian public sector. The 8% of nonconvergent Canadian BSs and their respective underpinning CIs contain no content that could be construed as beingcontext‐specificto the Canadian public utility sector. Implications of these study findings for HRD research and practice are discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".