Anticipating and Addressing the Politicization of Research
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
We examine an underaddressed issue in organizational research, the nature of the politicization of knowledge and its consequences for conducting research. Drawing on an illustrative case from a PhD research study and the underutilized theory of politicization, we go beyond previous work on politics in organization and management research to offer three contributions. First, we develop a process model underscoring the potentially emergent and interwoven nature of the politicization of research. In particular, we suggest politicization be seen as a trajectory of moments of difference in which researchers may or may not be aware of the potential political significance. Second, we offer four analytical resources to help researchers make sense around why politicization may occur: disputes over the “ownership” of knowledge, clashes of representational logics, ideological differences, and identity struggles. Third, we argue that politicization can be a catalyst, rather than an obstacle, for knowledge production and propose ways of anticipating and negotiating differences. Our aim is to raise awareness of the importance of understanding and anticipating the politicized situations researchers may encounter in their work.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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