Rethinking the distinctions between old and new media: Introduction
Bibliographic record
Abstract
Recent approaches to media change have convincingly shown that distinctions between old and new media are inadequate to describe the complexity of present and past technological configurations. Yet, oldness and newness remain powerful ways to describe and understand media change and continue to direct present-day perceptions and interactions with a wide range of technologies – from vinyl records to artificial intelligence voice assistants such as Siri and Alexa. How can one refuse rigid definitions of old and new, while at the same time retaining the usefulness and pertinence of these concepts for the study and analysis of media change? This introduction to the special issue entitled ‘Rethinking the Distinctions between Old and New Media’ aims to answer this question by taking up the notion of biography. We argue that the recurrence of oldness and newness as categories to describe media is strictly related to the fact that interactions with media are embedded within a biographical understanding of time, which refers both to the life course of people or objects and to the narratives that are created and disseminated about them. Employing this approach entails considering the history of a medium against the history of the changing definitions that are attributed to it and, more broadly, to considering time not only as such but also against the narratives that make it thinkable and understandable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".