<i>Mocking Bird Technologies: The Poetics of Parroting, Mimicry, and Other Starling Tropes</i>. Edited by Christopher GoGwilt and Melanie D. Holm. New York: Fordham University Press, 2018. Pp. ix+293.
Bibliographic record
Abstract
Given the aural element in the subject, the book could have benefited from the inclusion of sound, even visual notated sound, literal birdsong rather than merely the concept of birdsong, which runs the risk of becoming yet another human-centered metaphor. I also query why the plates are gathered together at the end; they would better support the paralinguistic interpretation if they were scattered through the volume or layered with the essays. This is possibly a publisher’s limitation, but it is unfortunate. Finally, the book suffers from some editorial inattention that leads to grammatical errors such as the illogical “equally as” (twice), and a misused main verb that leads one sentence to say the opposite of what it means, or to such solecisms as “F.R.S.” meaning “Friend of the Royal Society” when it should clearly be “Fellow.”
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".