Bibliographic record
Abstract
Take a suitcase full of your thirsty mountains, they will thrive in that rain. Pack the voices of your neighbourhood in a musical box, firmly locked for the long journey. Choman Hardi For this is the place the world ended. Miroslav Holub Yes, and who can turn away? Claudia Rankine Two days before the fire at Grenfell Tower, I found myself at the scene of the London Bridge attacks. I was retracing the steps of a family journey along the riverbank, taken two years before when my younger sister visited from Canada. The attacks took place on 3 June 2017, while the country was getting ready for a snap general election and the English: Shared Futures committee was in the final stages of preparation for our conference in July. I was out of London at the time. By the time I returned, on 12 June, the election had passed and we had become used to seeing crowds of mourners at vigils and multi-faith ceremonies in Westminster, Manchester and London Bridge. I had known about, but not set out to find, the tributes, flowers and prayers, the pink and yellow Post-it Notes covering the concrete isosceles monument outside London Bridge station, and the banks of flowers on the pavement in front of Evans Cycles. In the cobbled alleyways around Stoney Street the police cordon was still in place. Chipboard covered up shattered windows, and parts of the road were marked off with blue and white police tape. I wandered into the cool stone interior of Southwark Cathedral and found myself sitting in a service of thanksgiving. The church was busier than I'd ever seen it, with volunteers and local residents speaking to each other animatedly in corners, signposting people to the garden, or the service, or just stopping to sit quietly. I felt the air reverberate with shock as members of the church and the local community came together to pray and weep, along with tourists who – like me – had not planned to take part. I listened to the tales of horror and courage, saddened yet grateful that no one I knew had been affected.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.040 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.009 | 0.033 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".