Bibliographic record
Abstract
Critical Toponymy: Place names in political, historical and commercial landscapes contains a selection of double-blind peer-reviewed papers from the 4th International Symposium on Place Names that took place 18-20 September 2017 in Windhoek, Namibia. These papers present current thinking on how the critical turn in social sciences is manifested in toponymic research, not only locally but also internationally. As such it includes research on place names from South Africa, Namibia, Zimbabwe, Austria, Slovenia, Central America and even the former Czechoslovakia. The contributions show that the etymology of place names are never purely linguistic – social, political, commercial and other factors influence the giving, use and adaptations of these linguistic and cultural artefacts. Furthermore, given their high symbolic content, place names also serve as political and commercial currency. Place names are therefore important symbolic markers in preserving or changing cultural identities, and in marking or facilitating socio-political changes and relations. Critical Toponymy showcases the many ways in which the representational potential of place names can be deployed in different contexts. Scholars as well as practitioners in toponymy and sociolinguistics will find this an illuminating read.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".