Bibliographic record
Abstract
This paper returns to the relationship of “narrative versus database” (an argument originally made by Lev Manovich in 2001) as one that can be further addressed. A specific issue persists in text analysis research in the digital humanities: the difficulty of representing the figurative meaning of narratives through digital tools. Towards an accommodation, this paper adopts a narratological framework in order to propose alternative models of content management and organization that more closely resemble figurative meaning making in human language. These alternative models therefore better allow for the computational representation of figurative elements that N. Katherine Hayles describes as “the inexplicable, the unspeakable, the ineffable” of narrative literature. This paper argues that the construction of figurative meaning through paradigmatic substitution (as part of an imaginary vocabulary that is drawn from in the process of meaning making) is difficult to account for in the relational database—arguably still the most culturally prominent database model. By focusing on NoSQL (“no” or “not only” Structured Query Language) databases, this paper explores how layers of figurative meaning can be represented together through these flexible and non-relational models. In particular, the ability of non-relational databases to group together multiple values—encouraging their association, comparison, and juxtaposition—can be analyzed as a computational albeit imprecise counterpart to the formation of paradigmatic and figurative meaning. Thus, towards accounting for a word, image, or idea’s layers of meaning as expressed in literature, this paper offers a study of the limitations of digital tools and their critical negotiation with humanities research and reflection.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".