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The social dynamics of attracting talent in Halifax

2010· article· en· W1548178558 on OpenAlexaffvenueabout
Jill L. Grant, Karin Kronstal

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDiversity (politics)Public relationsDynamics (music)Locale (computer software)Creative industriesContext (archaeology)Quality (philosophy)Social dynamicsSociologyPsychologyPolitical scienceSocial sciencePedagogyGeography

Abstract

fetched live from OpenAlex

The paper reports a case study of factors attracting and retaining talented and creative workers in Halifax, Nova Scotia. All categories of workers interviewed mentioned quality of place and amenities in discussing their location preferences, but that could not fully explain their choices. For some occupations (like health research), talented people followed jobs; in other sectors (like music), talented workers migrated to a sympathetic locale with the right conditions for creative engagement; creative workers in some occupations (like those in architectural, engineering or planning consulting) were more rooted in place. The social dynamics—that is, positive and collaborative social networks within key sectors and a wider community perceived as welcoming and interesting—make this mid‐sized city attractive to talented workers. Local universities and a vibrant music scene generate a mutually reinforcing context that attracted mobile talented and creative workers to the city. Respondents noted Halifax's limited cultural diversity but did not report a perceived lack of tolerance as affecting their choices. In smaller cities, the social dynamics of place and workplace and the quality of life available may play more significant roles than tolerance in attracting and retaining talented workers, challenging a basic assumption of creative cities discourse .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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

Citations32
Published2010
Admission routes3
Has abstractyes

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