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Record W2590364288 · doi:10.1108/ijssp-05-2015-0054

Determinants of participating in training: a Canadian-based analysis

2017· article· en· W2590364288 on OpenAlexaffabout
Robert D. Weaver, Nazim Habibov

Bibliographic record

VenueInternational Journal of Sociology and Social Policy · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTraining (meteorology)Affect (linguistics)OriginalityInvestment (military)Value (mathematics)LogitLogistic regressionDemographic economicsAdult educationPsychologyPolitical scienceGeographyEconomicsSocial psychologyMedicineStatisticsPedagogyPolitics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to estimate and compare the across-time individual and contextual factors influencing the participation of Canadian residents in adult education and training during the 1990s and the early twenty-first century. This era is characterized by the social investment state (SIS), a policy paradigm adopted by various developed nations throughout the world, including Canada, during the latter part of the twentieth century. Design/methodology/approach The authors analyzed data obtained from the 1994, 1998, and 2003 versions of the Adult Education and Training Survey, which is administered by Statistics Canada. They employed binomial logit regression so as to predict the likelihood of the respondents participating in training. Findings Participants whose level of education was below the post-secondary level were less likely to participate in training, as were adult residents of households in which pre-school children also lived. These findings occurred across all three periods of data collection. Furthermore, urban residents exhibited an increasingly greater likelihood to participate in training across-time. Research limitations/implications Future studies should consider the funding source for training, be it from the public or private sector, and how this may affect participation. The impact that various types of training have on employment and earning patterns in developed nations should also be further assessed. Originality/value This study, with its use of the most recent available data to analyze across-time changes in the determinants of participating in training in Canada, has contributed to the knowledge base regarding the SIS in Canada and how it compares to its European counterparts.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.267
GPT teacher head0.508
Teacher spread0.241 · 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 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

Citations2
Published2017
Admission routes2
Has abstractyes

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