Delhi ke momos mast hote hain: Constructing the city through food
Bibliographic record
Abstract
L'articolo esplora la foodification della città tramite un'indagine sui social media. Esso mira ad una comprensione concettuale della foodification, che combina gli aspetti materiali, sociali e culturali del cibo e la sua mobilitazione. Utilizzando gli interventi su Twitter lungo 10 anni (2010-2020) a Delhi (India) sui momos (sorta di ravioli), il documento dimostra (i) l'uso di prodotti alimentari popolari per promuovere i produttori, propagando così la foodification, (ii) che i cittadini utilizzano il cibo come mezzo per sottolineare l'identità cittadina e (iii) per alimentare discorsi che vanno oltre il cibo. Il documento utilizza queste tre discussioni empiriche per costruire una più ampia comprensione dei veicoli egemoni di conoscenza nella concettualizzazione della città. Si dimostra come la città è costruita attraverso il cibo e il discorso, le pratiche e le performance che lo circondano.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one teacher head, 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".