Developing a Compendium of Ideas on Using the Retrospective Approach to Mine for GIS Nuggets
Bibliographic record
Abstract
The compendium of ideas paper addresses two needs: 1) Involving more people in the GIS retrospective program; 2) Creating an initial compilation of ideas which promote mining the various literatures – public, learned, popular (media), professional, etc. – for nuggets such as new ways to add to GIS technology, new reasons to add to geospatial information, and, new uses of GIScience research methods. Four design principles (connecting “ideas” and “nuggets”, using a modular approach, limiting modules to those critical to launch the project; and making it easy to modify modules) provide clear directions throughout the compendium-building process. And, each of the four modules (ideas about doing; ideas about objects of attention; principal GIS components as ideas and spawners of ideas; and, ideas as questions and questions as ideas) can be oriented to pursue general or particular interests that are held by all users of GIS technology and GIScience methods, techniques, and operations.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".