Læsernes holdninger til journalisters stavefejl i digitale medier
Bibliographic record
Abstract
Folketinget 1. Indledning Sprogrøgtere hævder jævnligt, at danske journalister staver dårligt eller ligefrem rædderligt.I læserbreve og på sociale medier harcelerer de over den sproglige standard i journalistiske medier og udstiller fadæser, der latterliggøres og generaliseres til stavefærdighederne hos den samlede journaliststand.Her ses et typisk eksempel på en klage over journalisternes stavning, i dette tilfælde fra en frustreret seer til TV 2's seerredaktør: Tænkte om du ikke kunne påbegynde et intenst arbejde omkring indførelse af STAVEKONTROL for de stakkels hjælpeløse journalister på TV 2|NEWS, som konstant og hele tiden opfinder nye ord, som for den almindelige befolkning er aldeles uforståelige.Alligevel insisterer ordblindesamfundet på TV 2 på, at udbasunere deres inkompetence på skærmen i tide og utide.Forleden opfandt de ordet "INDNU".I dag står menuen på "STATN".INGEN i min omgangskreds aner, hvad ordene betyder.(TV 2 2010) Seeren gør sig her til talsmand -muligvis i hyperbolsk forstand -for, at ordene er uforståelige, og i den offentlige sprogdebat genfinder man flere steder argumentet, at journalisters stavefejl støjer og forstyrrer i en sådan grad, at man som læser ikke blot bliver irriteret, men også
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.468 | 0.277 |
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".