Practicing Context in Organizational Research: An Auto-ethnographic Model for Manager-Scholars
Bibliographic record
Abstract
This theoretical methodology paper examines the practice of context in management research, and the unique role that can be played by researchers with a managerial background. Context often appears as an ephemeral framing device, drawing an imaginary line between the researcher and research field. We argue that the line between researcher and research field can be blurred or even erased through the research lens of a manager-scholar. In contrast to the customary pursuit of a sanitized objectivity, researchers can generate novel insights by embracing the messiness of context. By creating a methodological model for autoethnography by a manager-scholar, we theorize that the practice of context occurs as a radically reflexive process in two moments of time, one as manager and the other as scholar. The researcher is left vulnerable to the messiness of context, sharing the results not as a sanitized narrative, but recursive contextualization.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".