At Issue DEVISING UNIFIED CRITERIA AND METHODS OF MONITORING ANTI-SEMITISM
Bibliographic record
Abstract
Awareness of the increase in racist and anti-Semitic violence has led the Organization for Security and Cooperation in Europe (OSCE) and the European Union (EU) to settle agreements to monitor and combat the phenomenon. A European working definition of anti-Semitism and another on all forms of hate crimes will assist states to devise unified criteria for inclusion in their monitoring. Some governments, however, are failing to abide by the agreements into which they have entered to monitor such crime, including anti-Semitism. This paper examines these agreements, the reports that are published on combating anti-Semitism, and the reasons why the fifty-six governments concerned (OSCE membership overlaps with that of the EU, but also includes the U.S., Canada, and the former Soviet Union) are falling short in fulfilling their obligations. It notes the valuable work of NGOs, which are increasingly encouraged to provide data and expertise in monitoring, and concludes with an examination of the UK model, which is cited as fulfilling all the requirements, as well as the work of the Community Security Trust (CST). It ends with a call for consistent and objective reporting at the government and Jewish community levels. The Need for Unified Criteria and Methodology Since the 1980s, Jewish community groups in Europe have been monitoring anti-Semitic incidents. In most cases this is because police and other criminal justice agencies have failed to do so, and because they wished to draw public attention to the mounting violence against their communities. Two academic institutes, the Stephen Roth Institute at Tel Aviv University and the Center for Research on Anti-Semitism at the Technical University of Berlin, have also monitored incidents and trends. Jewish community groups and some independent academic contributors have been sending their reports to the Stephen Roth Institute since 1994 for inclusion in its annual report, Antisemitism Worldwide. Broad incident classifications have been agreed on over this period. However, they are of a somewhat loose and non-technical nature, bearing only a vague resemblance to the crime and incident classifications recognized by criminal justice agencies. It was the dramatic rise in anti-Semitism worldwide during the late 1990s that forced the Organization for Security and Cooperation in Europe (OSCE) and the EU to confront the problem, each in its own way, and eventually to encourage monitoring by national criminal justice agencies. The rationale behind this was that the problem is increasingly trans-national and therefore requires a joined-up, Europe-wide approach to understand and defeat it. Jewish defense and advocacy organizations can therefore no longer rely on simply publicizing details of anti-Semitic incidents, as in the past, in order to galvanize criminal justice agencies to take action. The rise in racist violence in Europe, particularly the increase in anti-Semitism, requires those Jewish organizations that monitor anti-Semitism to adopt unified criteria and methodology for measurement and analysis if they are to effectively educate their communities and their leadership, to alert governments and international agencies and persuade them to take further, effective action. The information that Jewish bodies gather and record has to be delivered and quantified in a form that will now be acceptable to and useable by governments and political and law enforcement agencies if they are expected to take counter action. Governments and the relevant intergovernmental agencies now accept that anti-Semitism and violence against Jewish communities has increased since the start of the twenty-first century. The murder of Ilan Halimi in February 2006 in Paris, the torching of synagogues in France and the UK, the continuous cemetery and synagogue desecrations, and the rash of anti- Jewish graffiti throughout Europe, North America, and the former Soviet Union have finally roused governments to take action. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".