Bibliographic record
Abstract
Current digital archives of stereocards, a popular form of early photography, offer only 2D scans of the cards' fronts. Such archives make large numbers of stereocards accessible, retrievable and searchable, but lack the informed, interpretive guidance that non-specialist users might expect. They also omit the information on stereocards' backs and privilege the photographs on the stereocards without attention to context or interpretation. Drawing on techniques of new media "edutainment," my virtual exhibit contextualizes and interprets stereocards from the Queen's University Special Collections Library in a way that is friendly to non-specialist audiences while organically promoting humanities research through features such as textual popups and hyperlinks to sources (where available online or in QCAT). Animated GIFs of the stereographs allow users to see the image in three dimensions—something that was available to a 19th-century audience but not necessarily to a 21st-century one. My project looks beyond the popular assumption that new media seeks, or should seek, to uncritically reproduce the experience of past media. Rather, I examine how new media allows us to be critical of past perspectives and biases in ways that were previously unavailable—while still remaining critical of my project's own limitations and potential biases. With the advent of digital media and the Digital Humanities, I argue that the time is ripe to rethink new media's relationship to old media and the past, as well as how to communicate this knowledge in ways that push beyond traditional notions of an academic digital archive.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".