Introduction: Forgotten Food Histories of South Asia
Bibliographic record
Abstract
Heritage food is a boom industry in India and Pakistan today.Five-star hotels and fashionable restaurants tout menus replete with "lost recipes" and "gastronomic traditions," while colonial-era eateries -Karim's in Delhi being one of the most famousturn their history into franchise.Food festivals, too, from Lahore to Chennai bring historic flavours to a general public hungry for dishes with provenance.For those wanting to bring home "centuries old food traditions," bookstores stock a colourful array of cookbooks selling South Asian cuisine through the prism of kitchen stories or a royal banquet.To make or complement those recipes, handy online providers and trendy grocers alike market heirloom food products: from Sempulam Sustainable Solutions' "traditional organic rice" to Bengalaru-based Loafer & Co's "local grain, global bread" made with "ancient" grains.Since the runaway success of "Raja, Rasoi aur Anya Kahaniyaan" ("Kings, Kitchens and Others' Stories")heading for Season 5 in 2023 -Netflix has capitalized on this interest in "culinary traditions" to keep viewers hanging on for "more like this." 1 Vloggers and bloggers from Instagram to TikTok enrich their #foodporn with a spoonful of Wikihistory to win over subscribers and rack up the "likes." 2 Yet, as journalist Sourish Bhattacharyya noted way back in 2015, much of the hype around India's "lost recipes" and "heritage cuisine" is little more than "a lot of chatter.""We need historians," he concluded, if practitioners aim to do more than "scratch the surface." 3 Bringing historians into partnership with practitionersincluding heritage activists, writers, street vendors, performers, chefs and farmerswas at the core of the broader project out of which this special issue on "Forgotten Food Histories of South Asia" has emerged.In 2019, scholars and culinary experts from the United Kingdom, India, and Canada came together to frame an original program of publicly engaged research and global knowledge mobilization under the title: "Forgotten Food: Culinary Memory, Local Heritage and Lost Agricultural Varieties in India."This project successfully obtained funding from the Global Challenges Research Fund through the Arts and Humanities Research Council of the United Kingdom (2019-2023).The nature of that funding required the building of "fair and equitable partnerships" 1 On "Raja, Rasoi aur Anya Kahaniyaan" see https://www.imdb.com/title/tt6953924/. 2 This paragraph summarizes Siobhan Lambert-Hurley's opening column for Scroll's "'Forgotten Food' series"; There are additional references to Sempulam Sustainable Solutions (affiliated to Tamil Nadu-based social enterprise, the Centre for Indian Knowledge Solutions: https://www.utsc.utoronto.ca/projects/feedingcity/2023/02/26/center-for-indian-knowledge-systems-ciks/)and to an innovative Bengalaru-based bakery "Loafer & Co." Tanya, "From Farm to Bakery: Loafer & Co is baking with heritage varieties of rice & wheat." 3 Bhattacharyya, "Bringing ancient Mughal recipes."
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 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 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".