Leveraging DEI to Enhance Collective Creativity and Organizational Learning at Jiva Consulting
Bibliographic record
Abstract
This thesis explores how diversity, equity, and inclusion (DEI) can support the learning organization at Jiva Consulting, a small Calgary-based firm specializing in knowledge transfer within the energy industry. Grounded in a constructivist paradigm and guided by Appreciative Inquiry methodology, the research engaged current Jiva team members through a focus group, semi-structured interviews, and a collaborative dissemination process. The inquiry was guided by the central question: How might DEI be leveraged to enhance collective creativity and support Jiva Consulting as a learning organization? Six findings emerged, highlighting the importance of intentional DEI practices, distributed leadership, psychological safety, curiosity, vulnerability, and the influence of early socialization on equity values. These findings informed a set of prioritized, participant-informed recommendations designed to integrate DEI more deeply into Jiva’s organizational strategy, team dynamics, and learning systems. Key conclusions emphasized the importance of cultural alignment, shared accountability, and reflective leadership practices in sustaining DEI momentum. The inquiry also surfaced broader personal and systemic themes, including the emotional labour of inclusion, the role of family in shaping equity values, and the complexity of hiring for difference. Through this process, DEI was affirmed not as a discrete initiative, but as a relational and evolving practice embedded in how people learn, lead, and belong together. The thesis concludes with reflections on learning as ceremony, the role of community, and the possibility that meaningful change begins not with knowing, but with curiosity and care.
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 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.001 | 0.003 |
| 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".