Autoethnography: Research as Reflection, Inclusion and Empowerment
Notice bibliographique
Résumé
Autoethnography is a personal, reflective, qualitative research method. It is obviously associated with anthropology, but may be more widely used in sociology and it occasionally appears in research in other social science fields. In this method, the researcher and the subject of research are the same person. It requires the researcher to engage in deep, rigorous, and reflective examination of their own experience and then to systematically analyze that reflection, drawing connections to theory, society, and culture as they do so. The resulting analyses can take many forms, including: scholarly prose, poetry, narrative, dialogue, and more. For the last year, we have been leading a learning community of librarians in the U.S. and Canada exploring the potential of this method to enrich the scholarly discourse in librarianship. Librarians in the community have committed themselves to collaboratively learning about the method, and each member is also producing an autoethnography of their own, exploring themes of identity and professional practice in librarianship. Most of the librarians in the learning community teach information literacy and it is increasingly clear that this method has a great deal to offer teaching librarians. Teaching librarians understand the importance of reflective thinking and metacognition in the learning process and there is a long tradition of reflective practice already in place in this community. Autoethnography is a method that busy, practicing librarians can do well and rigorously to inform their practice. Practicing professionals must make decisions quickly and in the moment as they draw on a body of practice knowledge gained through experience to do so. Donald Schön calls this “Reflection in Action.” Autoethnography offers a way to capture and share that practice knowledge that is so important in the classroom. It is also important to reflect on experience after the fact – Schön calls this “Reflection on action” – and autoethnography supports this as well. It pushes the researcher to connect their experience to something broader, to draw in theory, culture, and social factors, to make deeper meaning. Heidi Jacobs articulates the significance of this for librarians who teach information literacy: “Praxis – the interplay of theory and practice – is vital to information literacy since it simultaneously seeks to ground theoretical ideas into practicable activities and use experiential knowledge to rethink and re-envision theoretical concepts”. Autoethnography makes it possible for voices that get drowned out in large scale aggregations of data to be heard. It provides a way for the lived experiences of all librarians to be included in the professional conversation. It is an inclusive method and offers the potential to understand the role of information literacy in an inclusive society – as well as the inclusivity within the practice community itself. We will reflect in this paper on the experience of leading this learning community and share the insights we have gained about the potential of this method to inform the professional conversation about information literacy.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,037 |
| Communication savante | 0,014 | 0,017 |
| Science ouverte | 0,003 | 0,017 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».