Bibliographic record
Abstract
I spent 14 days of intensive photographic research taking 10 000 photographs while travelling around the coast of Scotland. This includes the incredible architecture in ancient cities; amazing, magical landscapes of heather shrouded moorlands, expansive glens with grass covered hills and lowlands, and black and red mountains; and magnificent castles. Scotland is a part of my cultural heritage. This series of photographs is a merging of my artistic and academic skills as a visual arts researcher. It is similar to grounded theory (Glaser & Strauss, 1967) used for my academic research wherein I let Scotland tell me what photographs needed to be taken and my photographic eye knew when to take the photograph from my years of experience as a photographer. Each of the photographs tells a visual story. As I continuously edited my photographs for months while making files in folders I asked myself: What was my experience of Scotland? How can I represent this experience so that it has the feeling of what each inspiring photograph had when I took the shot? It is a reliving and recreating of experience while working with specialty silver papers and creating triptychs, diptychs and other layouts to photographically tell the stories. These 19-limited edition colour archival quality giclée photographic prints are the result of my photographic Scotland experience. An exhibition is a publication and the exhibition of these photographs is supported by LURF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.375 | 0.119 |
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".