Bibliographic record
Abstract
Astrid Lindgren holds a unique position within world literature, yet her enigmatic creative process has for many years been hidden in her impenetrable stenographed drafts and manuscripts. Because Lindgren through her employment at publishing house Rabén & Sjögren acted as her own editor and publisher, these drafts contain the entire creative and editorial – and to date inaccessible – process behind her literary works. With the purpose of unmasking Lindgren’s creative process as visible in her shorthand drafts and typed up manuscripts, this article applies the perspective of sociological editing (shaped particularly by McGann (1983) and McKenzie (1986) and more recently developed and applied for text genetic purposes by for example van Hulle (2008; 2014) and Gabler (2018)) to early results from The Astrid Lindgren Code. Focal points are 1) the secretarial skill of shorthand as the engine in Lindgren’s creative process, and its function in the interplay between the different production roles assumed by Lindgren 2) how the inaccessibility of her manuscripts, as well as the fact that Lindgren as author, editor, and publisher was her own ‘collaborator’ has contributed to reproducing an image of Lindgren as a solitary literary genius, or with Norwegian writer Alf Prøysen’s words, “a solar system of her own”.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".