Queen’s Market: a successful and specialised market serving diverse communities in Newham and beyond
Bibliographic record
Abstract
This report demonstrates the importance and success of Queen’s Market as a specialist, affordable food and textiles market, and a welcoming and supportive community asset serving migrant and ethnically diverse communities in Newham, east London, and beyond. It is one of three reports documenting evidence from the Markets4People research project1 about the wide-ranging economic, social and cultural benefits produced by three of the UK’s largest and strongest traditional retail markets. Building on previous research, the reports bring a new focus on the importance of markets from the perspective of their existing customers. In each of the three case study markets, the research involved a survey of 500 market users (capturing a representative sample in terms of age, gender and ethnicity), two focus groups with selected user groups and around 10 interviews with key local actors, to contextualise the findings. Queen’s Market was selected for this research as one of three exemplary case studies well-placed to demonstrate the wide-ranging economic, social and cultural benefits of traditional retail markets in the UK. Located in the heart of Newham, east London, Queen’s Market is a covered street market including 164 street trading pitches, 65 small shop units and 20 kiosks. Based in one of the UK’s most ethnically diverse and low-income communities, it plays an important role in providing access to affordable fresh food, as well as relatively low-cost trading opportunities. It is a listed Asset of Community Value, owned and operated by Newham Council, with a dedicated support group, Friends of Queen’s Market, active for over 15 years. Following Rokhsana Fiaz’ appointment as Mayor of Newham in 2018, th e Council began to address several long-standing issues affecting Queen’s Market and to explore redevelopmentoptions for the Market and neighbouring sheltered housing building, Hamara Ghar. In this context, our research has focused on understanding the existing customers’ experience of the Market, including economic, social and cultural aspects, as well as wider governance issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".