From Theory to Practice: Leveraging Appreciative Inquiry for Workplace Belonging and Collaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study employs the Appreciative Inquiry (AI) framework, a well-established organizational development intervention, as a transformative approach for fostering workplace belonging and employee collaboration within organizations. In essence, AI is grounded in a strengths-based approach, focusing on positive organizational transformation through collaboration. Drawing on the AI theoretical framework and its application, this study proposes a series of reflective questions for each phase of the AI 4-D cycle – Discovery, Dream, Design, and Destiny. These questions are designed to stimulate reflection, encourage meaningful dialogue, and promote active engagement among organizational members. By focusing on organizations’ strengths and successes, these reflective questions help guide organizations in evaluating current diversity, equity, and inclusion (DEI) practices, envisioning a shared future, and developing actionable strategies and initiatives that cultivate a culture of belonging and collaboration. This study discusses both the theoretical and practical implications of using AI to foster sustainable and inclusive organizational environments.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it