Bibliographic record
Abstract
Som følge av #MeToo-kampanjen gjennomførte presseorganisasjonene en spørreundersøkelse om seksuell trakassering blant norske medieansatte høsten 2017. I denne artikkelen presenterer vi hovedfunnene fra den delen som omhandlet journalister og redaktører (N = 3282). Vi ser på sammenhengen mellom uønsket seksuell oppmerksomhet og seksuell trakassering, hvem som sto bak trakasseringen, om saken ble varslet, og hvorfor den eventuelt ikke ble det. Undersøkelsen viste at én av fire medarbeidere hadde opplevd uønsket seksuell oppmerksomhet siste halvår, og at omfanget av uønsket seksuell oppmerksomhet i større grad enn enkeltopplevelser forklarte at den rammede følte seg seksuelt trakassert. Kjønnene hadde omtrent samme terskel for når omfanget ble opplevd som trakassering, men kvinner hadde en høyere skår fordi de opplevde mer uønsket seksuell oppmerksomhet enn menn. Sakene der en leder var involvert, #MeToo-sakene, utgjorde mer enn 20 % av hendelsene. Bedriften var blitt varslet i 14 % av sakene; andelen var spesielt lav i saker der en leder var involvert. Den vanligste begrunnelsen for ikke å varsle var at saken ikke ble opplevd som alvorlig nok. Deretter kom frykten for konsekvensene og skamfølelse.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.177 | 0.035 |
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".