Bibliographic record
Abstract
My poster takes as a case study a new senior undergraduate course I designed in conjunction with members of the Taylor Family Digital Library. This course asks students to examine archival sources alongside published literary texts, and to engage in a digitization project of selections from the archival fonds of various Canadian authors. The general goal of the course – entitled “Reading in the Canadian Archive” and currently underway in winter semester – is to bring to the classroom an awareness of the material conditions under which literature is produced. This course asks undergraduate students to not only integrate archival records in literary analysis but to contribute to the archive by institutional digitization projects based on their course readings. “Reading in the Canadian Archive” asks students to imagine “how to pursue scholarship into a future that will be organized in a digital horizon and how to integrate our paper inheritance in that new framework” (McGann 185). As the course offers a brief intervention into the practice of archiving itself, students come to recognize that such archival practices “are constantly evolving, ever mutating as they reflect changes in the nature of records, record-creating organizations, record-keeping systems, record uses, and the wider cultural, legal, technological, social, and philosophical trends in society” (Cook 29). My poster considers how close analysis of archival records might lead to increased undergraduate engagement with literary texts.
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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".