Bibliographic record
Abstract
based on qualitative methods. 1 With FBR’s acceptance rate of about 10%, this means that we have received and processed a great deal more qualitative submissions in total. My experience is that the quality of these manuscripts varies widely. When improvement is needed, I find that there are a number of common strategies I consistently suggest to authors in my editorial letters. This editorial article is primarily designed to combine these suggestions into one document. In addition, I asked a few others for their suggestions about how to publish qualitative research. In response to my request, I received excellent feedback from a number of FBR associate editors, reviewers, and authors who have experience with qualitative methods. I thank them all! Below you will find seven strategies that I believe capture most of the suggestions I received and that reflect my personal experiences. These strategies relate to publishing qualitative research—focusing on the point in time when a study is mostly completed and authors are beginning the process of developing a journal article; my hope is that attention to these strategies will help (especially new) qualitative researchers navigate the publishing process. This editorial builds on a previous FBR editorial by Ron Chenail (2009), titled “Communicating Your Qualitative Research Better.” I recommend this article in many of my editorial letters to authors, and continue to encourage qualitative researchers to read it. Chenail’s key messages are equally critical today as they were 5 years ago. My suggestions below about how to publish qualitative research link back to Chenail’s important points. Also, because I want to use examples to illustrate my points in this editorial, I have selected two recently published FBR articles that are excellent exemplars of qualitative research. One article is based on interview data, and the other is based on the qualitative analysis of text in documents. Although there are other types of qualitative data, interviews and documents constitute the data source of choice for the vast majority of qualitative studies submitted to FBR. These articles are two of my personal favorites from the many excellent qualitative studies published. The first is an article published by Carlo Salvato and Guido Corbetta (2013) on transitional leadership of professional advisors in family firms. It was one of the articles included in FBR’s special issue on Advising Family Enterprise and is based on in-depth interviews with key individuals in four family firms. The second article I use as an example is by two PhD students—Evelyn Micelotta and Mia Raynard (2011)—that is based on analysis of documents gathered from official websites of the world’s oldest family busi
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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 | 0.119 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.147 | 0.059 |
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".