Sustainable Jewellery Practice: A Creative Approach To Communicate Sustainability
Bibliographic record
Abstract
This creative research is based on an extensive review of literature that explores both environmental and social concerns in the jewellery industry, relating to the metal mining sector. Issues addressed include the processes and methods of the extraction of metals through mining, to the metal finishing of jewellery manufacturing. Existing studies have revealed that there is a lack of clarity in defining sustainability in the context of a studio practice for Independent Jewellery Designers (Ceschin, and Gaziulusoy, 2016; Fettolini, 2018). This study will be presented through a review of literature that was integrated into a conceptual framework, followed by practice-based activities. I used my own craft education and experience as an example in this practice-based research. Positioning craft practice in a research context can facilitate the reflection and articulation of knowledge generated from within the research-practitioner’s artistic experience. Conducting primary research through making, enables knowledge to become explicit as a means of visual evidence to support the written text. In addition to the paper, a collection of brooches have been produced and will be exhibited as the creative component of this project.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".