Bibliographic record
Abstract
“[A]mong the many symbolic resources available for the cultural production of identity, language is the most flexible and pervasive.” (Bucholtz & Hall, 2003, p. 369) This study explores how food celebrities (re)produce (gender, class, cultural) identities through variant choice. The corpus (3,704 adjectival heads) derives from 20 hours of televised cooking shows from 12 food celebrities from Canada, England, and the USA. The chefs are classified in five gendered culinary personas (male: chef-artisan, gastro-sexual, environmentalist; female: pin-ups and homebodies) following Johnston, Rodney and Chong’s categorization (2014). The two linguistic variables examined are degree modifiers preceding adjectives: intensifiers (really great, pretty sticky), and attenuators (a bit cold, a little different), as well as gradable adjectives (nice, beautiful). I use multivariate analysis to measure linguistic (syntactic position and adjective type) and social (gender, country, and food) correlations, as well as qualitative methods informed by work in the growing field of Food Studies (Ashley, Hollows, Jones & Taylor, 2004; Johnston et al., 2014; Naccarato & LeBesco, 2012, Prescott, 2012). The results indicate that the intensification rates (29 %) and the three most frequently used intensifiers (really, very and so) in televised cooking shows are similar to those found by other studies (e.g., Ito & Tagliamonte, 2003; Tagliamonte, 2008; Tagliamonte & Roberts, 2005). However, different from previous findings, the nice and construction takes the fourth place of frequency, attenuators appear well distributed and with an important role as food and cooking gradators as well as markers of culinary control. The results also reveal that −ly intensifiers mark masculinity among chefs, HEDONIST VALUE adjectives indicate sensual femininity among pin-ups, and really and TASTE adjectives are instruments of adequation (Bucholtz & Hall, 2005) used by gastro-sexuals to assimilate to homebodies. Although the skew towards ‘positivity’ is unmarked and common across languages (Rozin, Berman & Royzman, 2012), the analysis suggests that it may serve a purpose in the construction of cooking shows as ‘fantasies of transformation.’ Finally, this paper exemplifies how sociolinguistic and variationist analysis can help decode social hierarchies and constructs within fields and societies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".