Bibliographic record
Abstract
In “towards a theory of the periodical genre,” Margaret Beetham observes that “the material characteristics of the periodical ... have consistently been central to its meaning” (22–23). In particular, Beetham emphasizes, “the elation of blocks of text to visual material is a crucial part of ” the periodical’s processes of signification and the reader’s experience of making meaning out of its time-stamped yet open-ended issues (24). While this theoretical position underlies much excellent critical work in periodical studies, it is less evident in the electronic repositories on which research in the field increasingly relies. In this paper, I examine what it might mean to inform our digitization practices with a theory of the periodical hypertext as a remediated object. Focusing on the specific editorial problem of periodical pages decorated with textual ornaments, I take as my case study The Evergreen: A Northern Seasonal (1895 to 1897), a Scottish magazine scheduled for markup and publication on The Yellow Nineties Online. Making remediated Celtic ornament a structural feature of its aesthetic design and an integral expression of its larger political agenda, the Evergreen reminds us of what is at stake if our own electronic remediation practices are not adequate to the periodical objects we study.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".