Bibliographic record
Abstract
This essay describes an in-progress arts-based dissertation project that attempts “visual life writing” (Sinner & Owen, 2011) as photographic memoir. Spanning several years to date, the series addresses the idea of visually archiving the self through an everyday photographic ritual. Ruminations on loss, presence/absence, and the passage of time are given creative and emotional consideration from behind the camera. Through narrative reflections on my experiences as an artist, teacher, and researcher, the process of art-making is brought alongside the images themselves. Drawing as its subject matter selected scenes, encounters, and objects from everyday life, I share how the project came to be and discuss some of its challenges, influences, hopes, and new understandings. Methods including near-daily photo walks, rephotography, visual journaling, and creative writing are enacted in an effort to locate the potentially pedagogical within a search for memory (Ricoeur, 2004). Some issues related to photographic practice and research are considered, including camera ethics and the vulnerability inherent in memoir-based work. Presented as an offering in-the-making, this is an unfinished, ever-evolving passion project still in a process of being understood. A small selection of digital images from the Visual Memoir Project (VMP) series are included, all by the author.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".