How October 7th, 2023, changed fear and exposure to hate amongst Jewish and Israeli members of universities
Bibliographic record
Abstract
On October 7th (2023) nearly 1,200 persons were killed in Israel by members of Hamas. Since then, there are accounts of an increase in antisemitism and hate against Jewish individuals around the world, irrespective of their involvement with the ensuing conflict.We conducted a survey experiment using data from 202 members of universities in the United States, Canada and Europe who identified as Jewish or Israeli. Respondents were asked about their personal exposure to antisemitism, hate or antagonistic speech, and about their comfort level conducting everyday activities. In addition, we randomly assigned whether each respondent was asked those questions about the weeks before October 7th, the weeks after that day, or about recent weeks.We found extraordinarily high levels of exposure to hate and antagonism toward Jewish and Israeli individuals in the period prior to October 7, which was exacerbated significantly afterwards. In addition, a significant proportion of respondents no longer enjoy the same level of comfort expressing their culture or conducting simple daily activities (e.g., leaving their homes) as they did prior to October 7. According to respondents, institutions of higher education have been ineffective in addressing their safety concerns on campus. We conclude with a discussion of implications.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".