Recruiting Gen Z Workers to Ontario Municipalities: A Study of How Ontario Municipalities Can Improve Recruitment Strategies to Attract Gen Z Workers
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
The generational shift from Millennials to Generation Z (Gen Z) is perhaps the most critical generational shift for modern day municipalities. While Generation Y, Millennials, and the generations before them continue to play a key role in the direction and success of local government organizations. It is important to analyze the trends of the incoming generation of workers to ensure long-term success and prosperity. This research report revolves around the research question, “How can Ontario municipalities improve their recruitment strategies to attract Generation Z workers?” Ontario municipalities must recognize the values of Gen Z’s and reconfigure their external recruitment practices to better align with the generation’s values. Traditional recruitment practices being used by Ontario municipalities limit the number of potential candidates in Generation Z who will act and apply to the job opening. A brief overview of the study of recruitment is completed to outline the different elements, and legal obligation Ontario municipalities must consider. Through an analysis of the current literature available on the topic, this study recommends several suggestions for Ontario municipalities to consider adopting in order to recruit Gen Z workers. In addition, three case studies for different levels of local government organizations were completed to review their recruitment and hiring procedures.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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