Bibliographic record
Abstract
According to the literature, journalism has undergone a significant change in the Western countries during the last half century. From being mainly conventional journalism (event-centred information that addresses the who, what, when, and where of an event) a growing part of journalism goes beyond the conventional journalism and adds a dimension, where the journalists among other things emphasize "why" or provide additional information to the significance of the current phenomenon. This is suggested to be a “more intellectually ambitious journalism” (Fink & Schudson, 2013: 15), and to “represents a much larger change in the character of news than a reallocation of effort to investigative reporting.” (Schudson, 2018: 162). In literature it has been conceptualised as contextual reporting, interpretive journalism or explanatory journalism. However, despite the plurality of concepts it seems to me that the literature overlooks an important aspect. Journalists can answer the question of “why” be referring expert sources, which will be the conventional way of doing journalism, or they can explain the question by themselves. The latter kind of journalism not only changes the relationship between journalists and sources (Salgado et al., 2016: 51), it may also signify a degree of independent knowledge creation not present in the conventional journalism. This paper argues that the question of journalism’s contribution to the existing knowledge cannot be addressed properly without a concept of knowledge creation. I suggest the concept of inference, because inferences form a great part of scholarly practice in the creation of knowledge, and journalists may apply the same strategies of inference as scholars. Hence, inferential journalism is journalism, where the journalist adds with his or her own inference to the existing knowledge of a current phenomenon. The aim of this paper is to outline an inferential framework to address the creation of knowledge in journalism.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.119 | 0.001 |
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; both teacher heads agree on what is shown here.
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".