Bibliographic record
Abstract
Three recycled papers were made using waste printed packaging and leaflets. The three colours most commonly found in the packaging materials were separated and used to create the three paper colours - grey, white and brown. The paper colours and the flecks of other colours introduced suggested three materials - concrete, sand and wood. Photographs were taken of these materials - textured concrete of the Hunterian at Glasgow Uni, sand on Calgary Beach, Mull and bark from a tree in Pollok Park (plus a microscopic image of wood fibre). From these images, with the help of a little scripting, embossing plates and linocuts were made to create a set of three 3D prints. The creation of the prints was an exercise in adding value to a readily available domestic waste product - printed paper and card. A process of separating the coloured print from the base paper/card was developed and the paper and card then categorised by colour. This led to the creation of three distinct, heavily textured handmade papers which revealed their past through the flecks of colour (and often visible branding) apparent in the final paper. Scripting using the visual programming language Grasshopper, which is used within 3D modelling software Grasshopper, allowed the development of methods which were used to translate input images into 3D and 2D forms which were embossed and printed on the handmade paper. This adds layers of meaning and value to a material which had previously been considered waste.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".