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
Abstract The narratives we have about ourselves are important for our sense of who we are. However, our narratives are influenced, even manipulated, by the people and environments we interact with, impacting our self-understanding. This can lead to narratives that are limited, even harmful. In this paper, I explore how our narrative agency is constrained, to greater and lesser degrees, through a process I call ‘narrative railroading’. Bringing together work on narratives and 4E cognition, I specifically explore how using features of our socio-material environments to support and construct our narratives does not simply offer up possibilities for creating more reliable and accurate self-narratives (Heersmink 2020) but can lead to increasingly tight narrative railroading. To illustrate this idea, I analyse how digital technologies do not neutrally distribute our narratives but dynamically shape and mould narrative agency in ways that can restrict our self-understanding, with potentially harmful consequences. As such, I argue that we need to recognise that digital devices not only support narratives but work as powerful narrative devices, shaping and propagating the kinds of narratives that we self-ascribe and act in accordance with.
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) | 0.000 | 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 it