Review and Implications of the AutoCarto Six Retrospective Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A previous IJAGR paper, using the Retrospective Approach to Commemorate AutoCarto Six (Wellar, 2014), presented the reasons for using a retrospective approach to re-visit papers that were published 30 years ago (1983) in the proceedings of the Sixth International Symposium on Automated Cartography. This paper addresses four important topics that arise from producing AutoCarto Six Retrospective. First, in response to requests for more information about the “retro experience”, the research design of the retrospective project is reviewed in terms of lessons learned. Second, the contribution that the retrospective approach makes to “the literature” on the evolution of automated cartography, geographic information systems, computational geography, and related fields is explored. Third, several implications of the retrospective approach for the literature search and review component of theses, dissertations, academic productions, and research proposals, as well as plan, program, and policy evaluation processes in both the private and public sectors are outlined. And fourth, comments are made about applying the AutoCarto Six Retrospective experience to other commemorative events.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it