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Record W7077882975 · doi:10.25316/ir-20442

Leading with Social Wealth: How Relational Investment Shapes the Social Well-being of Remote and Hybrid People Leaders in Canada

2025· dissertation· en· W7077882975 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2025
Typedissertation
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessNeglectSocial mediaQualitative researchPerspective (graphical)Social relationshipSocial relationSocial capital

Abstract

fetched live from OpenAlex

This thesis explored how remote and hybrid people leaders in Canada experience social well-being, and how their efforts to activate, maintain, or neglect personal and professional relationships might influence their social wealth. This mixed-methods case study included a survey of 1,218 remote and hybrid leaders and 13 interviews with Sun Life leaders in Canada. Survey findings reveal that loneliness levels align with global averages and are significantly lower than those reported in the United States; trusted social networks are typically local (within 30 minutes); and “best friends at work” remain important, even in virtual contexts. Sun Life leaders report high levels of trust and reciprocity. Communication channels and caregiving responsibilities variably influence connection, while social media is deemed largely ineffective for building and sustaining relationships, despite frequent passive use. Qualitative findings reveal four emergent themes: (1) social needs evolve, with a post-pandemic shift toward reciprocal and intentional connection; (2) loneliness manifests subtly for leaders, as a quiet longing for pre-pandemic workplace dynamics; (3) leaders sustain tight circles and remain emotionally reserved in groups; and (4) rhythm and ritual are associated with building and sustaining social wealth. These findings offer practical insights for remote leadership and organizational design in shaping a more sustainable and socially healthy future of work.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.180
Teacher spread0.172 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

Explore more

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