Professional Learning and Inbetween Publishing: The Tasks of the Charleston Briefings
Bibliographic record
Abstract
Should the book and the journal article remain the primary forms of scholarly production in the digital age? That is a question asked by publishing scholar Kathleen Fitzpatrick. She proposes a role for “inbetween” work. Indeed, there is a history of “grey literature” in many fields and of the short book. And academic publishers are experimenting with the form. In this context, an explanation of the rationale for and origins of the Charleston Briefings illustrates the possibilities for experimenting with inbetween publishing featuring subjects of interest to librarians and professionals in allied fields. There follows an account of the genesis, planning, and composition of a forthcoming Briefing on the scholarly workflow. While the length of the Briefings may appear to be its defining element, how it manages its scholarly and educational tasks is the key to meeting its goals and the needs of readers. In this case “inbetweenness” can be an advantage for representing the subject’s timeliness and utility while managing the rapidly growing literature on its different dimensions, including what the digital evolution of the scholarly workflow means for library services.
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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".