Postcolonial DH: Critical Cartographies, Decolonial Archives, and Humanities for the Public
Bibliographic record
Abstract
The workshop connected the rapid and recent development of digital humanities (DH) work on our Binghamton University campus with the central themes of the Landscapes series: how to imagine, create, and convey more just worlds to broad audiences.Gil provided an overview of his approach to digital humanities and showcased several of his projects.He showed us how we can use (and build) our computer literacy skills to fight back against totalitarian regimes banning books, contest historical silences such as those of the enslaved on Caribbean plantations in the 18 th century and help those affected by inhumane and repressive immigration policies such as Donald Trump's "Zero Tolerance" policy of 2018.Gil then provided feedback on current digital humanities research and community archive/storytelling projects by faculty and graduate student participants, including Amanda Ortiz's dissertation in History discussed below.The workshop inspired participants to develop their digital humanities skills and engaged them as collaborators in knowledge production and storytelling.Gil began by defining digital humanities, which includes using digital tools such as computer programs and languages and digital methods such as data visualization, mapping, or text analysis to conduct research in the humanities 1 https://sites.google.com/binghamton.edu/
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".