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Record W1975204188 · doi:10.1108/00400910310459662

Internship and the Nova Scotia Government experience

2003· article· en· W1975204188 on OpenAlexaffabout
R. Bruce Dodge, Mary McKeough

Bibliographic record

VenueEducation + Training · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsSaint Mary's UniversityNova Scotia Department of EnergyNova Scotia Department of Agriculture
Fundersnot available
KeywordsInternshipNova scotiaGovernment (linguistics)Professional developmentMedical educationPublic relationsPolitical scienceBridge (graph theory)PedagogyPsychologySociologyMedicine

Abstract

fetched live from OpenAlex

This paper explores student and graduate internships. The roles and motivation of the intern and the academic, employer and professional associations that sponsor internships are considered. An examination of the “Career Starts” Program created by the Public Service Commission of the Province of Nova Scotia, in Canada serves as a case study to consider the application of internships, practical issues and objectives associated with such a program, and the experience of individual interns. This case is interesting, as a “collective agreement” element currently limits intern access to full time employment within the government. The impact of this limitation is contrasted with conventional programs established as a “recruitment pool”. Internships are seen as a critical component of individual development and for succession planning for professional and management staff, as well as development of specialized skills. Internships are seen as providing a bridge between academic preparation, and full participation in work or a professional association that provides benefits to the intern, the academic institutions and employers or professional bodies.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.625

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.003
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
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 head0.090
GPT teacher head0.387
Teacher spread0.297 · 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 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

Citations28
Published2003
Admission routes2
Has abstractyes

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