Content Management Systems and Journalism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A content management system (CMS) is a computer program used by news organizations or individuals to create, edit, organize, and publish journalistic content. The origins of modern-day CMSs date back to the process of newsroom computerization that started in the 1960s and to technological developments in web publishing in the 1990s. The latter have led to the creation of software that allows nontechnical users to easily publish content on the Web (blogging software and social media platforms). Consequently, the development of CMSs can be understood as a process of remediation, that is, the operation through which “new” media incorporate “old” media in a series of refashionings: modern-day CMSs still bear some traces of previous technological systems, as exemplified by the protean existence of the slug, a term that originated in the hot-type era and has carried into today’s digital software terminology. Journalism research has generally studied CMSs as being part of the technical infrastructure of media, through a socio-material approach. In that regard, digital technology is a black box wherein lurks the many tensions inherent to contemporary newsmaking. Opportunities to study such (invisible) infrastructure therefore arise whenever it dysfunctions, and research has focused on the various problems, obstacles, and impediments brought about by CMSs in newswork. More specifically, studying the CMS draws attention to issues related to the institutionalization of journalistic workflows (that become ossified in digital technologies), newsroom technologies constituting a complex system, the evolution of professional roles and hierarchies (and consequent power relations), as well as the agency and relative autonomy of software.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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 it