Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.035 | 0.053 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".