Bibliographic record
Abstract
While consumers in affluent countries are ever hungry for alternatives to the ‘Big-Food’ mainstream, critical scholars have raised serious questions about the meaning of ‘alternative’ food products. I explore scholarly critiques of alternative food, and argue against a binary approach that sees foods as either alternative or not alternative. Instead, I suggest the utility of taking a multifaceted, ‘family of issues’ approach that is both reflexive and materialist. The case of ethical meat is used to explore the myriad, often contradictory ideals contained within consumers’ search for alternatives to mainstream market options. Three cautionary lessons are put forward. First, the goal of producing myriad consumer alternatives is significantly hampered by the competing, and often contradictory demands of market forces. Second, the discourse of food alternatives uses a ‘win-win’ logic suggesting that consumer sacrifice or change is unnecessary; the challenge of reshaping, and even downgrading consumer expectations is a necessary, but tremendous challenge facing consumer projects for ecological and social change. Third, the search for eco-social alternatives cannot simply make consumers feel good about their purchases, but must address the material realities and limitations of niche markets, and the need for structural reform to the food system.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".