Starting teaching as a millennial: A generational view on early career teaching in Canada
Bibliographic record
Abstract
Drawing from an extensive pan-Canadian study that examined the differential impact of induction and mentorship programs on early career teachers’ retention, this article compares perceptions of the early and late millennial and non-millennial participants regarding induction support, mentorship, professional development, thriving, and teacher attrition. The results of our multi-generational comparative research demonstrated differences in distinctive values, group-associated attitudes, and life stage factors between the early, late, and non-millennial groups, albeit they were less prominent than often suggested in the literature on millennials. The article concludes with implications for theory, policy, and practice by considering intergenerational needs and differences in early career teaching. • Millennials represent a prevalent majority of early career teachers across Canada. • Induction and mentoring supports were significantly higher for late millennials. • Mentoring increased millennial teacher retention and professional development. • Late millennials reported highest levels of self-care and establishing boundaries for healthy living. • The differences between the early, late, and non-millennials were more nuanced than the extant literature indicates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".