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.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".