Literary value in the era of big data. Operationalizing critical distance in professional and non-professional reviews
Bibliographic record
Abstract
New phenomena such as digital social reading, instapoets, and the "rating culture" expressed in online reviews challenge traditional literary criticism in newspapers and journals. Millions of reviews on platforms such as Amazon or Goodreads are part of this culture of participation and a counterweight to professional criticism. At the same time, successful instapoets such as Rupi Kaur reject the expertise of the gatekeepers of "prestigious literary circles" and try to establish a direct connection with readers. The aim of this paper is to build the proper methodological framework to capture these changes in the current literary system. To do this, the phenomenon of online reviewing has to be contextualized within the history and the praxis of assigning literary value to literary texts, the so-called canonization. In addition, literary theory needs to be able to analyze quantitative data and to integrate numbers into its models (engaging in a procedure that is called operationalization).
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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 | 0.034 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".