Bibliographic record
Abstract
Recent scholarship in genre studies has extended its focus from studying single genres to multiple genres, as well as how these genres interact with one another. This essay seeks to contribute to this growing scholarship by adding a new concept, intermediary genre. That is, a genre that facilitates the “uptake” of a genre by another genre. This concept is designed to reveal a particular aspect of multiple genres: that one genre can be used to connect and mobilize two otherwise unconnected genres to make uptake possible. The concept is illustrated in case study of knowledge mobilization, an instance in which scientific research was used in the judicial system to inform public policies on eyewitness handling and police-lineup procedures. The case study shows how intermediary genres emerge, how they connect other genres, and how knowledge circulates as a result of such connections and affects policy decisions.
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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".