Knowledge creation through recommender systems
Bibliographic record
Abstract
The way materials are archived and organized shapes knowledge production (Derrida, J. Archive Fever: A Freudian Impression. Vancouver: University of Chicago Press, 1996; Foucault, M. L’archéologie du savoir. Paris, France: Éditions Gallimard, 1969; Kramer, M. Going meta on metadata. Journal of Digital Humanities, 3(2), 2014; Hart, T. How do you archive the sky? Archive Journal, 5, 2015; Taylor, D. Save As. e-misférica, 9, 2012). We argue that recommender systems offer an opportunity to discover new humanistic interpretative possibilities. We can do so by building new metadata from text and images for recommender systems to reorganize and reshape the archive. In the process, we can remix and reframe the archive allowing users to mine the archive in multiple ways while making visible the organizing logics that shape interpretation. To show how recommender systems can shape the digital humanities, we will look closely at how they are used in digital media and then applied to the digital humanities by focusing on the Photogrammar project, a Web platform showcasing US government photography from 1935 to 1945.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".