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Record W2553105111

Building Futures: Career and Community Development in Small New Brunswick Towns

2016· article· en· W2553105111 on OpenAlexaffabout
Fabrizio Antonelli

Bibliographic record

VenueJournal of New Brunswick Studies / Revue d’études sur le Nouveau-Brunswick · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsMount Allison University
Fundersnot available
KeywordsFutures contractWork (physics)SustainabilitySociologyCommunity developmentEconomic growthCareer developmentCareer PathwaysGender studiesGeographyPolitical sciencePedagogyEngineeringEcologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

Communities in New Brunswick are facing the reality of shrinking populations and outward migration. As post-industrial economies develop in Canada, a clear shift in geography is taking place: young people are leaving small communities in New Brunswick to live in larger urban centres. This study of two secondary schools explores the possibilities for career development and community sustainability in this environment. The two communities examined in the study differ in their geography. Bathurst is in the economically depressed north of New Brunswick, while Sackville is in the expanding southern region near Moncton. Sackville also has a clear connection to knowledge work, as it is a university town. This study examines how youth in New Brunswick navigate their career development with respect to their family and community and asks whether knowledge/creative work can help to sustain small Maritime communities. The findings, based on interviews with teachers and focus groups with students, indicate that young people have a clear desire to stay in the province; however, students and teachers also realize that the possibilities for career development in their respective communities are limited and the reality of outward migration looms in the future for many young people.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.105
GPT teacher head0.274
Teacher spread0.169 · 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.

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

Citations0
Published2016
Admission routes2
Has abstractyes

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