1 Maybe it’s because I’m a Londoner? Place, Locality, Nationality and Identity
Bibliographic record
Abstract
In a society in which a majority of people are at least two generations removed from the habitual, if not always comfortable, relationships with soil, kin and community, many seek the security of some myth of commonality involving an association of geography and identity to quell emotional turmoil with safe simplicity. A few have sought to dig into the sources of anxiety to grasp its complexity in fictional and discursive works. My hometown, London, has provided the occasion for much of this searching. Ball (2004) has completed an excursion through Imaginary London, examining the viewpoints of Anglo Canadians, a Jewish Canadian, Caribbean Asians and Blacks and a variety of people from the Indian subcontinent, ending with the Anglo-Caribbean fused perspective of Zadie Smith (2000) in White Teeth. This ensemble is billed as “postcolonial ” in time and “transnational ” in geography. Unfortunately its publication missed
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".