The Jewellery Quarter Industry Cluster - halting terminal decline and providing practical support to Birmingham’s original creative industry
Bibliographic record
Abstract
This survey was commissioned to review how best to support further growth in the Birmingham Jewellery Quarter Industry Cluster through the development of a Creative Incubator and Hub. Data was collected from firms operating within this area to support the development of a 'Creative Incubator and Hub', defined as “an organization designed to accelerate the growth and success of existing and entrepreneurial companies in JQ-IC through an array of business support resources and services that could include physical and virtual space, capital, coaching and skills development, common services provision, networking connections and promotion.” \nThis survey builds on an earlier survey conducted in 2016 and aims to: \n1. \tCarry out deep-dive research to map the interconnected nature of the Jewellery Quarter Industry Cluster (JQ-IC ) and the links between the companies involved. This will establish key connections, components, strengths and weaknesses in the value chain. We will look to establish any issues with skills gaps. The survey is intended to enable us to identify which parts of the cluster need strengthening whilst also identifying potential for growth, start-ups, relocation and promotion \n2.\t Conduct a detailed options appraisal, including consultation with relevant stakeholders, to establish the best model for a JQ Creative Incubator \n3. \tConduct a fundraising options appraisal for the delivery of the recommended model(s) \nThe survey was funded by the Greater Birmingham and Solihull Local Enterprise Partnership and conducted by the Institute for Design, Economic Acceleration & Sustainability (IDEAS) at Birmingham City University, on behalf of the JQDT and the Jewellery Quarter Cultural Action Zone.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".