SAUDADE: An Analogue Father’s Digital Rebirth
Bibliographic record
Abstract
This research-creation project brings together a written component and a series of visual experiments under the title SAUDADE: An Analogue Father’s Digital Rebirth. The visual experiments document my pursuit to create a digital doppelgänger of my deceased father by employing Generative AI technologies to replicate his voice and animate still photographs—enabling him to speak and make facial expressions. The result is a series of self-reflexive, posthumous correspondences between us—narrated letters in which we reflect on grief, our parallel lives, our family’s emigration from the Azores to Montreal, and, of course, his uncanny digital rebirth. These exchanges are illustrated primarily using personal family archives. Whereas the visual experiments explore my attempt to revive, better understand, and interact with my father, the written component serves to contextualize this process. It offers a deep exploration of grief through the lens of the Portuguese concept of Saudade—a profound, nostalgic longing for something or someone beloved yet absent (Wikipedia). At the same time, this research aims to assess the potential of generative AI tools to produce strong emotional responses in viewers and to preserve the personal histories of the deceased, using their own voice. I reflect on how the process has reshaped my relationship to memory and clarified the motivation behind my long-term commitment to preserving my family image archive. I examine the intimate relationship between the still and moving image, and confront the ethical complexities of using generative AI to bring my father back for these purposes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".