Bibliographic record
Abstract
Over the last 10 years a new theory in the interpretation of cartography has taken shape. In her overview of the various interpretative approaches that have variously contributed to the present critical approach, the author identifies semiotics as one of the trails principally worth pursuing. A semiotic approach, namely a theory of cartographic semiosis, effectively shifts the emphasis from maps intended as a mediation of territory to maps taken as agents, whereby actions to be carried out on territory are determined. This perspective may be defined as cartographic hermeneutics, since it undermines the very semiotic notion of map analysis: the study of maps relies not on autonomous semiosis but on a second level (or meta-semiotic) semiosis that is deeply rooted in and strictly related to first-level, territorial semiosis. In particular, the author focuses on two concepts: self-reference and iconization. The former, which constitutes the core of cartographic communication, is used to indicate the map's ability to be accepted as such (by its mere existence) and to communicate independently of the intentions of the cartographer. The latter is the communicative process that results in circumstances and contingencies being communicated as truths (thanks to the self-referential nature of the map). Hence, as a model, the map does not represent territory but replaces it. Iconization means that direct knowledge of the world is sidelined, with the greater relevance being given to the knowledge generated by the map itself.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".