Institutional Responses to Threats and Harassment of Academics: Evidence From a Survey Among Political Scientists in Norway
Bibliographic record
Abstract
ABSTRACT In an era marked by increasing polarization, academics face growing risks of harassment and threats, particularly when engaging in politically sensitive research or public discourse. This study investigates these challenges through a pilot survey of political scientists in Norway, focusing on harassment prevalence and institutional responses. Findings reveal that a small but significant share of surveyed political scientists reported experiencing harassment or threats over the past 5 years. Harassment frequently occurs digitally, with social media and online campaigns as common avenues. Institutional support appears inadequate, with few respondents indicating satisfaction with their institutions' guidelines for handling such issues. The study underscores significant negative impacts on academics' mental well‐being, safety perceptions, and professional engagement. It also highlights the broader chilling effect on academic freedom, where fear of harassment deters scholarly inquiry and public participation. These findings stress the urgent need for universities to enhance support frameworks and safeguard researchers' well‐being, particularly those investigating controversial topics. Future research aims to extend this analysis across different national contexts, to better understand the relationship between harassment of scholars, institutional arrangements, political discourses, and academic freedom.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".