The Emergence and Consequences of Voice Climate
Bibliographic record
Abstract
The objective of this thesis was to further the nascent paradigm on team-level voice, specifically voice climate and team voice. First, in Study 1, I examined how and why voice climate emerges in teams. In particular, I proposed that leaders stimulate shared perceptions of voice climate depending on how they previously responded to voice (i.e., voice acceptance or rejection). In turn, I proposed that voice climate enhances teams’ subsequent voice, as mediated by team risk, fear, efficacy, and vitality. I tested these propositions with a between-subjects team experiment, in which I manipulated a confederate leader’s responses to their team’s voice, and assessed its effects on team affect, cognitions, and subsequent voice. Next, in Study 2, I conducted a multi-wave training experiment to explore whether we can train leaders to successfully encourage their teams to speak up. First, I developed a one-hour training program that focussed on leader openness and responsiveness to voice, based on insights from the voice and leadership training literatures. Next, I randomly divided 65 students into either a 1-hour voice or control condition, and administered the training. Finally, approximately one week later, these students participated in a 1-hour team task, after which their team members rated leaders’ openness and responsiveness to voice, as well as voice climate and team voice. Finally, in Study 3, I investigated whether, how, and why voice climate ultimately affects team functioning by focussing on the mediating mechanisms that link voice climate to team learning and performance. In particular, I proposed that voice climate enhances team effectiveness through its sequential effect on negative and positive team affect (i.e., fear and vitality), cognitions (i.e., risk and efficacy), and voice (i.e., quantity and quality). I assessed these propositions with multi-sourced field surveys with 59 teams from 8 Canadian companies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".