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Record W3006312722 · doi:10.1002/9781119142973.ch23

Training Teachers in Academic Mentoring Practices

2020· other· en· W3006312722 on OpenAlexaff
Simon Larose, Stéphane Duchesne

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMentorshipTUTORMedical educationProfessional developmentProcess (computing)PsychologyQuality (philosophy)PedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This chapter presents the theoretical and empirical foundations for the Accompagnement par des Enseignants du Secondaire (ACCES) Program. It describes ACCES Program main components and some preliminary findings of the evaluation process. The chapter provides a discussion of the potential mechanisms that operate in these types of programs. It outlines the ACCES Program based on two theoretical models: the mentoring sociomotivational model and the systemic model of mentoring. The presence and quality of communication between the mentor and parents is characteristic associated with effective academic mentoring. Schools, communities, and professional organizations consider formal mentoring to be a useful and advantageous preventive approach. Mentor recommends that supervisors regularly touch base with a significant person in the mentee's social network, such as a parent, tutor, or teacher. Besides the initial training and mentoring supervision, the mentorship duration, and status of the mentor are generally considered factors for generating positive effects of mentoring.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.181
GPT teacher head0.426
Teacher spread0.246 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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