Footnotes, Endnotes, and HTML5: Blogging and the future of literary criticism
Bibliographic record
Abstract
Conference paper presented at the ACCUTE (Association of Canadian College and University Teachers of English), Calgary 2016. \n\nLiterary scholars were among the earliest adopters of digital dissemination of research; indeed, the field of Digital Humanities is populated heavily by people with PhDs in English, especially from areas like Renaissance literature where a large body of material is available in open source formats. As the Digital Humanities have expanded and other fields have embraced digital culture for dissemination of information, new issues around publishing and peer review, including the utility of open access journals, have emerged. These venues for publication and conversation offer a democratic approach to scholarly debate, often engaging academics and non-academics alike, and demanding acknowledgement of fan communities and their unique approaches to the close readings of texts. This intersection can frustrate traditionally-trained academics, but it can also enrich academic conversations and help connect the scholarship of literature to the real-world experiences of readers.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".