Bibliographic record
Abstract
have received and managed many manuscripts based on qualitative research methods. In the past 5 years (2009-2013), almost 15 % of articles published in FBR were 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 manu-scripts varies widely. When improvement is needed, I find that there are a number of common strategies I con-sistently 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 expe-rience 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 jour-nal article; my hope is that attention to these strategies will help (especially new) qualitative researchers navi-gate the publishing process. This editorial builds on a previous FBR editorial by Ron Chenail (2009), titled “Communicating Your
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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".