MétaCan
Menu
Back to cohort
Record W2472764624 · doi:10.1108/et-01-2016-0016

Skilled trades to university student: luck or courage?

2016· article· en· W2472764624 on OpenAlexaffabout
Bonnie Watt

Bibliographic record

VenueEducation + Training · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHabitusWorkforceOriginalityCertificationPedagogyBachelorPsychologyValue (mathematics)SociologyPublic relationsMedical educationManagementSocial psychologyCreativityEconomic growthPolitical scienceSocial scienceEconomics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to examine participants’ experiences as they transitioned from the skilled trade labor workforce to the school teaching profession. Their goal was to work in the secondary school system as certificated teachers. Design/methodology/approach – The study examined interview data from a 2014 to 2015 evaluation study of participants in the Career and Technology Studies Bridge to Teacher Certification Program in Alberta. Interview comments of 20 participants were analyzed. Findings – Participants earning a bachelor of education degree countered their skilled trade habitus with adjustment to the university habitus, with support provided though the program and strong networks among the students. Individuals demonstrated resiliency, persistence, and optimism. The findings may have significance more broadly for a re-examination of university policies and spaces for non-traditional students. Originality/value – The paper provides an insight into how a well-designed program provides opportunities for individuals to transition from the skilled trade workforce to university. Further, the paper contributes to the scholarly literature in the area of second-career teachers’ habitus, fields, and capitals.

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)0.003
metaresearch head score (Gemma)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.008
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.003
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 head0.086
GPT teacher head0.389
Teacher spread0.304 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueEducation + TrainingSame topicTeacher Professional Development and MotivationFrench-language works237,207