Bibliographic record
Abstract
The Found Polaroids project started in 2011 with the finding of 484 images and has grown into a personal archival collection of over six thousand Polaroids. e concept behind the project is to breathe new life into long-forgotten images by asking creative minds to write stories about them. e project simply asks for 250-350 word flash-fiction submissions; not of who these people are, but who they could have been. e project has since become a hub of collaboration between photographers, writers and academics advocating for the cultural importance of material photography and found photography. Much of this exchange and collaboration has come about through digital pathways and is part of the material turn facilitated by online exchanges.What makes this collection unique is that most shots are entirely candid and were captured by someone who had a personal relationship with the subjects in the picture. In that sense, each comes coupled with a story that can really only be told by those in front of or behind the camera—but these stories have been lost. Initially, I was fixated on knowing the true stories, but slowly it dawned on me that the importance of stories is not always in their literal truth, but rather in the truth that is reflected in our own lives within these stories. A really great story is simply one that holds a mirror up to our own reality.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.315 | 0.127 |
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".