Millennials and Public Service: An Exploratory Analysis of Graduate Student Career Motivations and Expectations
Bibliographic record
Abstract
Canada's public service workforce is aging, and all levels of government will experience a large number of retirements in the coming years. In an increasingly competitive labour market, governments face challenges in attracting and retaining new recruits, particularly among the “Millennial generation,” who are entering the workforce, and whose outlook on work appears to differ significantly from previous generations. What motivates Millennials to pursue a public service career? What are their expectations concerning the benefits of a career in the public sector? This study explores these questions through a qualitative analysis of written statements of Canadian Master's students seeking a career in the public service. The findings demonstrate that Millennials are motivated both by perceived intrinsic benefits, such as the opportunity to make a difference in society, as well as extrinsic rewards, such as opportunities for career advancement. In addition, many identify a public service career as a “calling”—a sense of obligation to contribute to the public interest— and the analysis reveals various events and experiences that inspire this “call to serve.” Implications for public sector recruitment and retention of Millennials are presented.
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.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".