Staying Motivated: The Study Protocol for a Life-Course Analysis of the Career Paths of Canadian Public Servants
Bibliographic record
Abstract
All Canadians benefit from a high quality of public service delivery, which is directly related to the motivation of public servants. A top priority for governments is to attract and retain a public sector workforce that is motivated and enthusiastic to serve the public good. The Public Service Motivation (PSM) theory offers a framework for the analysis of motivations of public servants. Knowledge is lacking on how motivations of public servants change over time and are shaped by the life-course dynamics of identities, roles, inequalities, and experiences. Our goal is to chronicle the motivations of individuals over a life course, specifically from the onset of their career in the public service to their current career stage. We will contribute to PSM theory by combining it with the Life Course Theory (LCT). This will allow for the analysis of change in PSM over time and in relation to other life events. We aim to further our understanding of determinants of motivation of public servants by unveiling the dynamic, gendered, and diverse nature of PSM, and its interconnections with professional and personal lives. Our approach is qualitative. We will interview 100 alumni of graduate public administration programs in four Canadian institutions with reference to their graduate program admissions letter as a point of departure. During reflexive interviews, participants will co-analyse their admissions letters, where our role will be to guide them along the PSM framework. We will hear from study participants how their motivations changed since those expressed in their admissions letter, to what extent motivations over time were influenced by the events in their professional and personal lives, their identities, and roles in the workplace and in society, and their personal characteristics. Interview transcripts will be analysed using reflexive thematic analysis and interpreted jointly with the study participants.
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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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.014 |
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".