What Is Needed to Enable a Cultural Shift in the Market Research Department at the Gangler Company?
Bibliographic record
Abstract
This thesis investigates how to create an environment for organizational change within the Market Research Department at the Gangler Company (a US-based consumer products company). I explore what is influencing the current cultural environment and which of those influencers can be shifted to encourage organizational change toward the “ideal” culture that the organization has identified. Using new institutionalism as the theoretical approach, I discuss the significance of institutional forces (such as the economy and the rise in technology) on the cultural elements (i.e. behaviors, ideas, material artifacts and social structures) in the Market Research Department. Lastly, I show that by understanding those institutional influences, I can better assess what cultural elements can be shifted and which cannot. Of the cultural elements that are able to be shifted, I recommend three interventions that the organization should employ: 1) from a contrive culture to a culture of candor, 2) from a culture of division to a culture of cohesion, and 3) from a culture of knowing to a culture of learning.
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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".