Bibliographic record
Abstract
When I started my blog Novel Readings in 2007, I did not expect it to affect my academic practices at all, much less become one of those practices itself.1 In retrospect, it seems inevitable that blogging, which fosters a spirit of open inquiry, exchange and conversation, would after a while make the conventional forms of academic research and writing feel constricting. Less predictable is that this discomfort with specific academic habits would prove so productive or so profoundly alter my general outlook on academia. Academic research has become defined by depth and specialization; I have (re)discovered the value and pleasure of breadth and exploration. Academic publishing proceeds glacially; I have learned the stimulation of immediacy. Academic publishing is also insular; blogging reoriented me towards the fundamental purpose of scholarly writing: communication – or what we now more elaborately call ‘knowledge dissemination’. Perhaps I sound like an evangelist for blogging as the ‘scholarship of the future’. I am not. I do not think every academic should blog, and I certainly do not think blogging should replace all the other ways in which we carry on our work as intellectuals and educators. Blogging will neither suit nor serve every academic nor every academic purpose. I am convinced, though, that academic blogging can and should have an acknowledged place in the overall ecology of scholarship. It does contribute – and should be recognized as contributing – to both the intellectual and the institutional goals of our universities.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".