What I See and What You Read: A Narrative of Interdisciplinary Research on a Common Digital Object
Bibliographic record
Abstract
This paper presents the dual narrative of a shared research combining approaches from LIS and literature studies. Content and textual analyses of the digital novel The Unknown help identify areas of common interest, such as genesis and access. Interdisciplinary issues, such as methodology and reporting styles, are also addressed.Cette communication présente la bi-narration d'une étude partagée combinant des approches des sciences de l'information et des études littéraires. Des analyses de contenu et textuelles du roman numérique The Unknown aident à relever les zones d'intérêts communs, notamment la genèse et l'accès. Les questions d'interdisciplinarité, telles que la méthodologie et le style, seront aussi abordées.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.029 | 0.070 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".