Creating Powerful Stories: What Scholars Can Learn from Filmmakers
Notice bibliographique
Résumé
Writing compelling and impactful academic articles is hard. For years, senior scholars and journal editors have urged us to combine rigorous research with vivid writing. Engaging the reader requires narratives that are convincing, reflexive and imaginative. Yet, the reality is that most academic papers are rather formulaic and far from engaging. Sure, we cite each other a lot, but do we really enjoy reading each other’s work? And if we don’t, are we surprised that practitioners and the public cannot be bothered to look at anything we produce? Well, the good news is that we can do a lot to make our writing more exciting. As a mid-career academic, I decided three years ago to get into filmmaking alongside my academic work. I was curious about exploring film as a means of expression and storytelling. Through my work with film and other filmmakers, I have made a revealing observation: scholars and filmmakers often care about similar things. They seek to understand the human condition and they often dedicate their work to social and environmental causes. Also, both scholars and filmmakers rely in their work on strong narratives. They use powerful examples to tell a bigger story. Yet, good films seem more effective than most papers at telling stories of importance that entertain, while also making a lasting impression. This essay discusses how filmmakers do it, what scholars can learn from it, and what actions they can take to improve their ability to write engaging manuscripts. The power of filmmaking seems obvious. For example, films can create a strong emotional impact through audio-visual storytelling. This is also why academics increasingly use visuals, such as photographs, in their articles to add emotional richness to their narratives. Yet, I have also learned about other – less obvious – ingredients of effective storytelling in film, which, in combination with audio and visuals, could inspire academic writing as well: multi-layered storytelling, the use of characters, and building stories from scenes. All three aspects may not only help better engage academic audiences, but also generate a wider impact with academic research, which I discuss at the end of this essay. In sharing my thoughts and experiences, I will focus on three films I have been involved with: “Finding Simon”, a short film, which I directed as part of a documentary film training and which I successfully submitted to several film festivals in 2021. The film is about a Brighton-based artist, his life and relationship with his work; “The Oldest Dance” (executive producer), a short fictional film by Laura Girvent Alcalde about the role of consent in sex work; and “Finding Ubuntu” (contributing producer), a documentary film by Annette King about the advocacy and community work of a Congolese refugee.
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 | 0,023 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,019 | 0,032 |
| Communication savante | 0,035 | 0,053 |
| Science ouverte | 0,004 | 0,019 |
| Intégrité de la recherche | 0,009 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,006 |
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 ».