Media Informatics: Theory, methods, and tools
Bibliographic record
Abstract
Abstract Panel abstract: Although the full range of new media has yet to be defined, traditional media such as journalism, music, film, photography, sculpture, theater, the written and spoken word, performance and installation art are all morphing as digital, socially networked technologies present new opportunities. Hybrid new media objects and environments are emerging that blur distinctions and pose new challenges to information science researchers. The immense scale and rapidity of the transition to digital spheres is clear but the multi‐modal consequences are only beginning to be explored. Media Informatics is the study of how humans seek, use, share, manipulate, store, retrieve, and organize digital multimedia. Closely related to Informatics and to Media Ecology, Media Informatics studies the behaviors and practices related to new media objects and environments including social, political, entertainment, communication and information aspects of new media content in order to design and develop tools for media access, retrieval and storage. The intent of this panel is not to argue for the establishment of media informatics as a formal discipline in need of its own association, etc. Instead we argue that media informatics is the direction to which traditional informatics is evolving and we make the case for greater affinity with media ecologists as collaborators in this evolution. This panel will present an overview of media informatics including theoretical frameworks, tools, methods and research applications in current use. The first presentation outlines the background, scope and methodology of media informatics, while each of the subsequent three presentations deals with a specific new media informatics research project.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".