Underlying values and competencies of public and private sector managers
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
Purpose The purpose of this paper is to identify unique values and competencies linked to private and public sector environments. Design/methodology/approach This study is based on critical incident interviews with a sample of senior leaders who had experience in both the public and private sectors. Findings The findings illustrate distinct public and private sector relevant competencies that reflect the unique values of their organizations and the character of the organization’s environments. This paper suggests a range of distinct public sector competencies including: managing competing interests, managing the political environment, communicating in a political environment, interpersonal motivational skills, adding value for clients, and impact assessment in decision-making. These were very different than those identified as critical for the private sector environment: business acumen, visionary leadership, marketing communication, market acumen, interpersonal communication, client service, and timely and opportunistic decision-making. Private sector competencies reflect private sector environments where goals need to be specifically defined and implemented in a timely manner related to making a profit and surviving in a competitive environment. Public sector competencies are driven by environments exhibiting more complex and unresolvable problems and the need to respond to conflicting publics and serving the public good while surviving in a political environment. Originality/value A key message of this study is that competency frameworks need to be connected to the organization’s unique environments and the values that managers are seeking to achieve. This is particularly important for public organizations that have more complex and changing environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it