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Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,009 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle