“Academic Writing: Writing and Reading in the Discipline and Academic Reading: Reading and Writing in the Discipline” by Janet Giltrow
Bibliographic record
Abstract
Janet Giltrow's second edition of Academic Reading and third edition of Academic Writing are a handy set for writing and rhetoric courses and an essential for every composition library.The books mark an important advance over Giltrow's already classic earlier editions.Both volumes offer more than their titles may suggest.Academic Reading, apart from providing an abundance of materials and ideas for writing classes, gives insight into the functioning of social "interest" in academic discourse.Academic Writing, on the other hand, by far outstrips a traditional writing handbook.Rather than merely recommending proper or effective style and organization, it problematizes the underlying assumptions of such recommendations.Giltrow's approach is radically cognitive and social.She invites the reader to a thoughtful inquiry into the functional underpinnings of the genre of academic writing.Building on the idea that "style is meaningful", the author insists on the inherent and complex connectedness between thought, discourse, and society: I would argue that there is no surface in writing, and that the stylistic qualities which tax our capacity to name them are more than skin-deep.And, moreover, I would suggest that the "originality" academics readers value depends on styleon the typical ways of speaking that produce certain kinds of knowledge of the world.(Academic Writing, p. 9)
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".