"Authentic and Amazing": authenticity as an evaluative category in online consumer restaurant reviews
Bibliographic record
Abstract
Claims and evaluations of authenticity are a powerful resource in food discourse: reviewers use evaluations of authenticity to demonstrate their expertise, and restaurants viewed as authentic receive higher star ratings. But the multivalent nature of authenticity presents challenges for researchers. This contribution seeks to understand authenticity by combining computational and corpus driven discourse analysis methods. O'Connor et al. (2017) sought to quantify the impact of authenticity on consumer perception via four theoretical authenticity types (type, craft, moral, and idiosyncratic). This method is tested using a sample of US restaurant reviews and compared to sentiment analysis metrics computed from the same dataset. All types except for moral authenticity showed a positive effect on sentiment. Authenticity in restaurant reviews is further investigated by examining collocates of terms referring to authenticity and compiling keywords of subcorpora created from high and low scoring reviews. Reviewers most often topicalize authenticity in terms of place, taste, and descriptors of ethnicity. These findings illustrate how combining corpus driven discourse analytical and computational methods can illuminate evaluation from multiple perspectives and provide insights which may help to improve computational approaches in the future.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".