Bibliographic record
Abstract
I am searching for a way to grieve someone I never knew. At age 26, I was lucky enough to meet the woman who would become my wife. We quickly discovered that there were many coincidences and connections that could be found when we examined our lives a little more closely – our parents shared a wedding anniversary, our fathers each had five siblings, Alice’s parents shared their names with my grandfather and his second wife (Walter and Joan). But what quickly became apparent to me were the links between Alice’s mother and my grandmother. Apart from photographs and memories shared by those who knew them, I would never know them. Both lives ended tragically young. Both died from genetic diseases. Photographing, for me, is to write a metaphor. There are things unphotographable – how do you create an image of someone who died a quarter of a century ago, a person you have never known? In varying sizes and at varying heights, my photographs act as constellations within which relationships begin to form independently of the connections that I draw between them. By searching for visual pleasures in the world around me using multiple formats of photography, I make visual the abstract histories that are known to me about Joan and Jean. Through examinations of heredity and meditation on coincidence and predetermination, I cling to what will inevitably be lost, trying to view grief not as something that passes, but something we are always in the midst of.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.772 | 0.629 |
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".