For the Record: Journalism Recording Technologies from “Fish Hooks” to Frame Rates
Bibliographic record
Abstract
James Carey famously repositioned the spotlight on the techniques of journalism itself as a practice devoted to the task of defining “what is to be considered real: what can be written about and how it can be understood.” This article looks at shorthand, the tape recorder, and the cameraphone as material objects that shape journalism as a practice even as they, in turn, are discursively constructed by and situated in journalists’ quest to establish their authority to define, in Carey’s words, what is real. A historical study of recording technologies ultimately demonstrates both a continuing desire to escape the “imperfect medium” of the human body in favour of one that is able to better select, process, and store information (Kittler, F. 1999. Gramophone, Film, Typewriter. Edited by G. Winthrop-Young. Stanford University Press), and the ultimate futility of that desire. If they fail, it is at least partly because those tools are already embedded with certain values of who is human, who is trustworthy, and who is or can be objective.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".