Bibliographic record
Abstract
The big difference between non-spatial and spatial data is the absence of geographic coordinates; however, non-spatial data frequently does have some kind of geographic reference embedded in it such as an address, postal code, place name, etc. Geocoding is the process by which non-spatial data with this type of implicit geography is converted into geographic coordinates or linked to a geographic space. Geocoding enriches non-spatial data because it provides researchers with additional possibilities for visualization and analysis. This paper reviews the current methods being used to geocode both structured and unstructured data as well as some of the tools, including open source ones. It also documents the ways in which researchers are repurposing their original non-spatial data through geocoding. Finally, it discusses the importance of geocoding as a service offered by data centers and libraries.
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 | 0.015 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".