MétaCan
Menu
Back to cohort
Record W4408366958 · doi:10.1016/j.tate.2025.104997

Starting teaching as a millennial: A generational view on early career teaching in Canada

2025· article· en· W4408366958 on OpenAlexafffundabout
Benjamin Kutsyuruba, Keith Walker, John Bosica, Rebecca Stroud Stasel

Bibliographic record

VenueTeaching and Teacher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of SaskatchewanQueen's University
FundersSocial Sciences and Humanities Research Council
KeywordsPedagogyPsychologyMathematics educationSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.371
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueTeaching and Teacher EducationSame topicTeacher Education and Leadership StudiesFrench-language works237,207