Bibliographic record
Abstract
With recent technological advances, lab-grown meat has been touted as a non-violent, environmentally friendly meat alternative. However, the success of lab-grown meat depends on how it is framed and perceived. This paper argues that lab-grown meat is a metaphorical translation, a rewriting, that aims to function in Turkish society. To investigate the manipulation of the patronage and the reception of lab-grown meat, a thematic analysis is conducted on a corpus of data representing public perception and the positions of various social actors that may influence the acceptance of lab-grown meat. The data includes public social media posts, news media articles, government announcements and interviews with scientists, as well as blog posts and videos from science YouTubers. The findings suggest that while political actors often engage in “translation as resistance,” effectively hindering the acceptance of lab-grown meat, the media and intellectuals play a pivotal role of “translation as mediation.” They work to recontextualize lab-grown meat positively, thereby crafting a more favorable public perception. This dynamic results in a spectrum of responses within Türkiye, ranging from opposition to acceptance. This paper argues that the metaphor of translation offers broader insights into the challenges of introducing novel foods, highlighting various cultural and political factors involved in the process. Translation can play an immense role in redefining the exploitative aspects of our food production and has the potential to reshape or perpetuate existing norms within the food industry.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".