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Record W2778639889 · doi:10.25316/ir-106

Walking the talk : leading change-related communications in Girl Guides of Canada–Guides du Canada, British Columbia Council

2017· article· en· W2778639889 on OpenAlexaboutno aff
Audrey Ying-Hu Wang

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGirlPolitical scienceGeographyMedia studiesLibrary scienceTelecommunicationsSociologyEngineeringPsychologyComputer science

Abstract

fetched live from OpenAlex

This interpretive phenomenological study used an appreciative inquiry-based methods approach to explore how Girl Guides of Canada―Guides du Canada, BC Council could leverage its organizational values to communicate and engage its volunteers in a positive change management experience. Leader-member exchange theory, network theory, and organizational culture theory provided the theoretical framework for data collection and analysis. Seven semi-structured interviews were conducted. Participants were purposively sampled from former and current members of provincial council. The dynamics of how these women experienced change within an organization that values the empowerment of women in leadership roles were explored. Key themes such as the importance of relationship building, the empowerment of members, and the opportunities to make a difference emerged as common values. Understanding these values can foster a change experience where the reasons for change are openly communicated, members feel like their voices are heard, and feedback is valued in the decision-making process.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.209
Threshold uncertainty score0.420

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0290.010
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.224
Teacher spread0.197 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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