Elevating the Lab Grown Diamond: A Critical Review of the Contemporary Jewellery Industry
Bibliographic record
Abstract
DeBeers' iconic 1938 “A Diamond Is Forever” campaign associated diamonds with everlasting love and singlehandedly constructed the value of, and public demand for, these gemstones (Epstein, 1982). This event is known as the “diamond invention” (Epstein, 1982). However, the purity of natural diamonds was challenged in the 1990s because of rising concern about blood diamonds (Siegel, 2009). In the same era, gem-quality lab-grown diamonds entered the market though they remained largely unknown (Kitawaki, Abduriyim, Kawano, & Okano, 2010). Recently, awareness of synthetics has increased given millennial values (IGDA, 2019). Today more jewellery companies have adopted lab-grown diamonds, many of which maintain the romantic associations from the diamond invention despite changes in social values over the last eighty-one years. The industry requires differentiation of natural and lab-grown diamond sectors for several reasons including to uphold diamond value (e.g. Sherman, 2014; Siegel, 2009a; Whiteley, 2016). Not much scholarship exists on the current state of the lab-grown diamond industry. Thus, this study delves into the debate between both sectors to devise a strategy for lab- grown diamonds, considering the current social climate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".