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Record W4307187297 · doi:10.3102/0013189x221122746

Has “Who Comes Back” Changed? Teacher Reentry 2000–2019

2022· article· en· W4307187297 on OpenAlexaboutno aff
Anna Moyer

Bibliographic record

VenueEducational Researcher · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)National Longitudinal SurveysReentryPsychologyRecessionDemographic economicsCohortRacial differencesRace (biology)Work (physics)Longitudinal studyPolitical scienceSociologyEconomicsMedicineEthnic groupGender studiesLaw

Abstract

fetched live from OpenAlex

Studies of early-career teachers in the 1970s–1990s find that one-quarter to one-half of teachers who left the classroom eventually returned and that returning was associated with teachers’ gender and their child-rearing responsibilities. However, much has changed in the last forty years. Women are more likely to continue to participate in the labor force after having children, and teacher labor markets have been impacted by federal policy (e.g., No Child Left Behind, Race to the Top) and the Great Recession. Using data from the National Longitudinal Survey of Youth 1997 (NLSY97), I find that only one-fifth of teachers who exited the profession from 2000–2019 returned. This is a substantially lower rate of return compared to similar work using a previous cohort of teachers from NLSY79. Furthermore, I do not find evidence that teacher reentry is associated with gender or child-rearing status. These findings have implications for teacher labor markets, as reentering teachers can expand the pool of experienced teachers.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.367
GPT teacher head0.475
Teacher spread0.108 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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