Navigating political minefields: Partnerships in organizational case study research
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to examine key challenges associated with conducting politically sensitive research within a workplace setting, and to highlight strategic partnerships that can be developed to address these challenges. METHOD: The author's research on employee mental health issues within a large healthcare facility serves as the foundation for identification and description of "political minefields" that investigators may encounter when conducting organizational case study research. Key methodological principles from the literature on qualitative case study research will frame discussion of how to understand and address political sensitivities in the research process. RESULTS: The benefits of conducting organizational case study research will be outlined, followed by discussion of methodological challenges that can emerge in negotiating entry, collecting data (gatekeepers, researcher reflexivity, participant authenticity and non-maleficence), and communicating research findings. CONCLUSION: Courage, collaboration and clear communication with stakeholders at all levels of the organization are critical to the success of workplace based case study research.
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 | 0.145 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".