“Det ligger inte riktigt i rollen att du pratar” : en kvalitativ intervjustudie om allmänreportrars erfarenheter av att bevaka olycksplatser
Bibliographic record
Abstract
A reporter is often required to report from different types of accidents, and this could be traumatic experiences. At the same time, a journalist is the only person who operates at these scenes without professional training in meeting persons in a state of shock. In the field of crisis coverage, there is much to learn regarding what consequences this will bring to the individual reporting from various accidents. This bachelor thesis includes eight in-depth interviews with reporters working at local newspapers. The aim is to examine the reporter's ability to justify their role in crisis coverage when working from scenes of accidents as a part of their working environment based on the theory of sensemaking. The results of the interviews show that experiences differ between the reporters, but all share similar norms on how a reporter is expected to act. A reporter is, as shown in previous studies, not expected to express emotions while reporting about emotions. However, the interviews showed that talking to others about experiences from scenes of accidents was important for all the reporters interviewed, although the type of person talked to varied. This means that if reporters are not given the opportunity to debrief experiences, it could, in the long term, lead to a situation where reporters are not able to justify for themselves their professional tasks during crisis coverage.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".