Bibliographic record
Abstract
What happens next? That was the question asked of early-twentieth-century authors Nellie L. McClung, L. M. Montgomery, and Mazo de la Roche, whose stories and novels appeared serially and kept readers and publishers in a state of anticipation. Each author answered through the writing and dissemination of further instalments. McClung’s Pearlie Watson trilogy (1908–1921), Montgomery’s Anne of Green Gables books (1908–1939), and de la Roche’s Jalna novels (1927–1960) were read avidly not just as sequels but as serials in popular and literary newspapers and magazines. A number of the books were also adapted to stage, film, and television. The Next Instalment argues that these three Canadian women writers, all born in the same decade of the late nineteenth century, were influenced by early-twentieth-century publication, marketing, and reading practices to become heavily invested in the cultural phenomenon of the continuing story. A close look at their serials, sequels, and adaptations reveals that, rather than existing as separate cultural productions, each is part of a cultural and material continuum that encourages repeated consumption through development and extension of the originary story. This work considers the effects that each mode of dissemination of a narrative has on the other.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".