Bibliographic record
Abstract
The spatial visuality of comics has received substantial attention from comics’ scholars and, more recently, from cultural and political geographers. These have shown how reading comics is an embodied, codified, learnt and culturally-situated activity. Viewer involvement takes place through the distinctive devices, vocabulary and grammar of comics: parts are observed while the whole is sensed and constructed. In this experimental academic comic, I explore how this active involvement might help orient critical geographical practices. Comics’ specific visuality makes readers labour to produce meaning, translating the spatiality of two-dimensional sequential images into four-dimensional narrative, what Dittmer has called ‘a map of time’ (2010). Methodologically, I use detournement (Debord 1956) to build a visual argument that combines a text-based scholarly literature review with a limited corpus of pre-existing images taken from two recent popular Italian comics to tell a story. Reading between images, texts and gutters makes concrete the paradoxical materiality of words and discursivity of images, while building upon a purposefully limited visual corpus. This dialogue of images and words results in a call for an empathic geography, connecting bodies and experiences visually, suitable for representing a fragmented world built upon making sense of a diversity of viewpoints.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 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 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".