Do Historical GIS and Digital Humanities Walk Hand in Hand?
Bibliographic record
Abstract
In a number of countries today, borrowing from a discussion that originated in Anglo-Saxon countries, there is an increasingly insistent discussion of historical GIS as one of the stars of the digital humanities galaxy. The reasoning has been addressed, albeit with some delay and distinctly by disciplinary sectors, in a number of contributions and congresses and has seen the prevalence of a substantial acceptance of this thesis that pegs historical GIS as a component of the macro-sector of digital humanities. An alternative reading on the real convenience of maintaining a rather disciplinary distance between both is offered for the purposes of cognitive growth. It is therefore not a question of establishing primaries or hegemonies of one over the other but rather a “call to arms” (historians, geographers, urban planners, archaeologists, ecologists, etc.) by leveraging the scientific heterogeneity (interdisciplinarity), offering space for common dialogue. Thus, through the uniting of different forces that individually remain partial and marginal in the use of historical GIS will it be possible to consider real growth that can enable an effective enrichment of geographical-historical-cartographic research based on the use of GIS in face of the digital humanities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.064 |
| Scholarly communication | 0.022 | 0.059 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".