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Record W2975067577 · doi:10.5038/2577-509x.3.2.1057

Contextual factors in early career teaching: A systematic review of international research on teacher induction and mentoring programs

2019· review· en· W2975067577 on OpenAlexafffund
Benjamin Kutsyuruba, Keith Walker, Lorraine Godden

Bibliographic record

VenueJournal of Global Education and Research · 2019
Typereview
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsQueen's UniversityUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMentorshipConceptualizationSituatedPsychologyAffect (linguistics)Professional developmentPedagogyContextual learningRepresentation (politics)Context (archaeology)PoliticsMedical educationPolitical science

Abstract

fetched live from OpenAlex

Early career teachers (ECTs) are situated in a dynamic contextual landscape that both influences their development and practice and dictates professional expectations for instruction and professional learning. This systematic review of international research literature sought to establish the understanding of teacher induction and mentoring program support of ECTs through the following research questions: 1) which nations and regions are represented in research literature that details formal or programmatic support of ECTs? 2) what international research evidence is there to describe various contextual factors that affect experiences of ECTs? and, 3) how do teacher induction and mentorship programs respond to the various contextual factors affecting ECTs? Upon detailing our review method and sampling procedures, we synthesize the convergences and divergences of the findings within each of the contextual factors. The conceptualization of contextual factors in this review included social, political, cultural, organizational, and personal forces that influence the professional practices of ECTs. Finally, we summarize the review findings in a heuristic model that offers a visual representation of the implications of our findings, and discuss the implications for policy, practice, and future research.

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)0.016
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.352
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.575
GPT teacher head0.605
Teacher spread0.030 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations37
Published2019
Admission routes2
Has abstractyes

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