Bibliographic record
Abstract
As already noted in the Introduction to this Special Issue, several databases offer vast amounts of information on conflict, wars, political violence, that are unseeable and incomprehensible to a wider audience before being selected, analysed, and communicated in an accessible and intelligible way.But several authors have questioned the objectivity of maps and the scientificity of the messages they convey. 1For, as argued by Harley, it is through this process of selection and analysis that mapsbecame more a reproduction of power relations than an objective display of textual and numerical information. 2Similarly, as Dodge and Kitchin point out, mapping is concerned more with creating than revealing knowledge -which means that maps are constructions intended to draw particular impressions, instead of being impartial reflections of processes, relations, and correlations between the phenomena and factors emerging in different parts of the world. 3 In this special issue of Journal of Regional Security, spatialised text and data on peace and security-related issues are presented by authors who, by mapping textual and numerical descriptions and offering explanations of them, demonstrate that a meaningful understanding of maps requires viewing them as a link in a chain of intertextual relations with the data used to create them.Seven contributions, by a variety of scholars, reflect the spatial turn that has been revived in security studies througha revolution of neo-geography that has completely changed and, to a large extent democratised, the cartographic representation of spaces and places having to do with international security. 4Although many of the tools that sparked this revolution have existed since the 1960s (e.g., the Canada Geographic Information System), their accessibility in recent years to individuals and non-expert communities has enabled them to represent space in previously unknown ways.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".