Bibliographic record
Abstract
The purpose of this article is to offer a perspective on adapting action research principles and methods in academic marketing research contexts. From combined theoretical and practical perspectives, the article provides a dialogical argument about the issues associated with implementing action research, addressing three important and related questions. First, are marketers specifically (and people in organizations, more generally) truly reflective? Is reflection suited to some organizations' authoritarian realities? Second, how is a strong organizational culture a barrier to change and further learning, and how might this difficulty be overcome by action research? Third, what is the role of the researcher in the process, and what skills, knowledge, and influence must this person have to successfully implement an action research program? The article concludes by proposing that an incremental orientation to change and intervention effectiveness is needed for these approaches to work in demanding marketing contexts.
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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 | 0.127 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".