MétaCan
Menu
Back to cohort
Record W2110948420 · doi:10.1177/103841620801700305

Social Justice and Career Development: Views and Experiences of Australian Career Development Practitioners

2008· article· en· W2110948420 on OpenAlexafffund
Mary McMahon, Nancy Arthur, Sandra Collins

Bibliographic record

VenueAustralian Journal of Career Development · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsAthabasca UniversityUniversity of Calgary
FundersCanada Research ChairsAthabasca University
KeywordsCareer developmentPsychological interventionSociologyPublic relationsSocial changeEconomic JusticeSocial workContext (archaeology)Social justicePerspective (graphical)PsychologyPolitical scienceSocial sciencePedagogyLaw

Abstract

fetched live from OpenAlex

Career development practice had its origins in social justice reform over 100 years ago. A social justice perspective requires practitioners to examine the environmental context of their work, including the social, economic and political systems that influence people's career development. Achieving socially just outcomes for clients may necessitate intervention in these systems. While social justice is receiving a resurgence of interest in the literature, little is known about career development practitioners' attitudes towards and knowledge of socially just practice. The present paper examines the views and experiences of Australian career development practitioners on social justice. Data was collected by means of an online survey. Participants offered descriptions of their understanding of social justice and also examples of critical incidents in which they had attempted social justice interventions. Findings related to how Australian career development practitioners describe and operationalise social justice in their work are presented, as well as recommendations for 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 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)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.010
Scholarly communication0.0060.004
Open science0.0010.011
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.128
GPT teacher head0.319
Teacher spread0.191 · 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

Citations28
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueAustralian Journal of Career DevelopmentSame topicCareer Development and DiversityFrench-language works237,207