Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
After two days of fruitful discussions in an international workshop on narrative, emotions, and identity at the University of Navarra, Pamplona, Spain, in October 2012, participants were convinced of the need to further explore the connections between these issues across the multiple forms of contemporary narrative. Though much has been written about narrative identity, this collection of essays privileges its possibilities from the perspective of theories of emotions. The following articles refer both to the ways in which emotions are represented in narratives, as well as how these representations assume a reader's emotional competence, dwelling on the numerous ways in which narrative empathy is enhanced. Through close readings of different contemporary narratives, this special issue illustrates the advantages of narrative in the portrayal of emotions: Emotions are, unlike language, non-linear, imprecise, unstructured and diffuse. Therefore language is an inadequate medium to represent emotions, and "telling," that is, putting a simple label on an emotional state, is less engaging than "showing" by a wide register of narrative means available to fiction. (Nikolajeva, 2014, p. 95)
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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 it