Bibliographic record
Abstract
Reportager Award 2015 \nhttp://reportager.uwe.ac.uk/award.htm \n \nThe reportager award was conceived and curated by Gary Embury. It is the first International reportage drawing award of its kind. Moleskine in Milan sponsored the award enabling 4 awards to be made. A professional award consisting of £600 prize and £1700 travel Bursary. A student prize of £300 and £1300 travel bursary. The professional recipient subsequently used the money to make drawings in Chandigarh in India. The student award enabled the artist to travel to Cuba to make a series of drawings on location. Two smaller special awards were made for work produced in a Moleskine journal. \nTotal Number of Entries. \n130 projects in total. Of which 69 were student entries. \nEntries received from United Kingdom, United States, Italy, Canada, Malaysia, China, Slovakia, Germany, Taiwan, Czechoslovakia, Spain, Australia, Norway, South Korea, Brazil, Egypt, Belgium, \nUganda, Portugal, Costa Rica, Mexico, Austria. \n Numbers accepted for exhibition. \n75 entries of which 39 were professional entries \n Number of individual original artwork and digital print for exhibition – 350
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.771 | 0.715 |
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".